Discrete event simulation in construction research has been used extensively to evaluate the performance of construction operations. Obtaining an optimal configuration of the decision variables using simulation alone requires the evaluation of all possible combinations of the values of these variables, which is not feasible for problems with a large search space. To this end, simulation is often coupled with an optimization algorithm in order to optimize the construction operations. However, the major drawback of the current stochastic simulation optimization methods is that they require a long computational time and may present inferior solutions in the final Pareto front. This research presents a DES optimization framework that can be used by decision-makers to enhance and improve the current practice of decision-making in construction projects. The aim of the framework is to select a set of near-optimal resource combinations that minimize the total project duration and total project cost. The objective of this research is to investigate the benefits of incorporating variance reduction techniques in discrete event simulation optimization within the context of simulation optimization of construction operations. Three variance reduction techniques are studied using a case study, namely, common random numbers, antithetic variates, and a joint application of the previous two techniques. The incorporation resulted in an average time saving of 81.81% while improving the quality of Pareto solutions by 2.4% and reducing the presence of inferior solutions by 63.15%.