Project planners are faced with a great challenge in determining the number of resources to use on a construction project. Therefore, the efficient planning of construction operations is deemed necessary to meet a project's objectives. Stochastic simulation optimization, which is the combination of stomachic simulation with optimization algorithms, has been proposed by several researchers to optimize construction operations. Traditionally, explicit averaging is used to estimate the objective functions of candidate solutions. This is carried out by calculating the average estimates of the objective functions obtained from a number of simulation replications. However, the computation effort increases as the number of replications and the size of the search space increase. The main objectives of this research are to improve the quality of the optimum solutions while reducing the computation efforts. To achieve that, this study investigates the feasibility of applying implicit averaging in construction simulation optimization problems. Implicit averaging refers to using a single simulation replication to estimate the objective functions. Using this method, the optimization algorithm will be able to evaluate a larger number of candidate solutions compared to explicit averaging in a finite period of time. This research's anticipated benefits include reducing the computation time and improving the quality of the optimal solutions. The initial results show promising advantages of using implicit averaging over explicit averaging.