This study was conducted on the use of design of experiment to optimize tractor hourly fuel consumptions during harrowing operation. Its aims to enhance the fuel utilization efficiency by a tractor harrowing operation to reduce operational cost and increase agricultural productivity. The optimization is necessary to minimize energy usage during harrowing operation, which leads to severe wastage and economic loss. The field experiment took place at the Rivers Institute of Agricultural Research and Training (RIART) Farm in Port Harcourt, Rivers State University. The experimental land measured 160 m by 38 m (4,480 m2) and was divided into three blocks, each with nine plots. Each plot was 50 m by 2 m, with a 1 m walkway between each plot for different treatment options, and a 2 m space between each block and 1 m at the sides of the outer blocks. The design consisted of 9 treatments with three replicates, and total number of 27 treatments. The field test parameters (harrowing depth, and tractor forward speed) and hourly fuel consumptions were measured according to their specific standards. The study used MINITAB 19 software to conduct statistical analyses of the general full factorial design (GFFD), including model fit adequacy, analysis of variance (ANOVA), main and interaction effects, multiple linear regression model, and response optimizer (Minitab Inc, State College, PA, USA). Also, the validity of the model was checked using standard error (SE), coefficient of determination (r2), Adjusted r2, and prediction r2. The Pareto charts of standard effect suggested that the impact of harrowing depth and tractor forward speed, and their interactions are statistically significant on the hourly fuel consumption during harrowing. The normal probability plot, showed that the fuel consumptions data during harrowing is approximately normally distributed, which satisfies the first condition of model fitness examination. Also, the histogram plot displayed an approximately normal distribution. Hence, this observation further supports the usual distribution of the tractor hourly fuel consumptions. The residual vs fitted value plots revealed that the data points for hourly fuel consumption data during harrowing are randomly distributed with no notable pattern, confirming the constant variance condition of the residuals. The residual points are also fully random, according to a plot of residual vs observation order. As the three assumptions were generally observed, thereby revealed that the multiple linear regression model generated could expresses the experimental results well for tractor hourly fuel consumptions during harrowing operation. Based on the statistical analysis, ANOVA in GFFD indicated significant difference with 95 and 99% confidence (P<0.05 and P<0.01 levels of significance) regarding the impact of harrowing depths and tractor forward speed and their interactions effect on tractor hourly fuel consumption during harrowing. Also, standard error of very negligible numbers was revealed for harrowing confirmed that the multiple linear regression could predict the experimental data correctly. The results of the coefficient of determination (r2) adjusted r2, predicted r2 are 99.88%, 99.81%, and 99.66%, respectively for hourly fuel consumption during harrowing. These suggest 99.66% of the variability in the dataset were explained by the estimated multiple linear regression model created for the tractor fuel hourly fuel consumption. Optimized tractor hourly fuel consumption during harrowing, was attained at harrowing depth 0.09 m and tractor forward speed of 5 Km/h. The study revealed the minimum tractor hourly fuel consumption corresponding to operating conditions (harrowing depth and tractor forward speed) requirement was 3.04 L/h harrowing.