Resource leveling is essential in construction management to eliminate the obstacles associated with large fluctuations in resource usage. The purpose of this thesis is to demonstrate three different optimization algorithms (the heuristic method, the genetic algorithm, and the ant colony optimization technique) for scheduling and resource leveling in the construction industry and identify which produces the best schedule that is also practical in the actual world. This will be done by applying the algorithms to a hypothesized project to illustrate the algorithms' concepts as well as to a real case study in Jordan to test whether the algorithms provide applicable results in real life. The optimization will be based on minimizing ?(M?_x), and using three other indices for evaluation purposes: ?(V?_m), (RR), and (RIC). The significant finding of this thesis is that conducting the optimization based on only one index cannot provide the best solution compared to when optimizing based on a combination of weighted indices. Furthermore, regarding the ant colony optimization technique, it is concluded that this algorithm gives an indication of how suitable the initial schedule is for obtaining optimal results. So, It is recommended to use a combination of performance indices associated with different weights and not depend only on one index in optimizing resource leveling and scheduling.