A review of the recent literature on the models that focus on resource leveling in Critical Path Method networks shows that different objective functions have been used to optimize resource utilization. The main objective of this study is to investigate the impacts of using different objective functions on resource utilization histograms in Critical Path Method networks. For this purpose, nine different resource leveling objective functions were optimized via a genetic algorithm-based model. The model was developed using actual data obtained from a steel framed industrial building construction project. It was found that each of these objective functions generates different resource utilization histograms. In order to determine the improvement levels achieved by resource leveling using nine different objective functions, the improvement percentage in each parameter and the average improvement percentage for each objective function were calculated. Even though the objective function that involves the minimization of the sum of the square of the deviations in daily resource usage provided the best average improvement percentage in the studied case, another objective function(s) may provide better average improvement percentage in different projects. The contractor should consider all objective functions for resource leveling and select the one(s) that provides the best average improvement percentage.