In this study, we have aimed to optimize patient flow in emergency departments while minimizing associated costs. In order to be able to compare the effect of any changes, we developed a simulation model for an emergency department using Queuing theory, and regarding the optimization we utilized Genetic Algorithm to find the best change. Basically, we have designed a Discrete Event based, multi-class, multi-server queuing network as we have considered the emergency department a set of stages associated with a queue of patients waiting to be served. Each stage has multiple service providers such as Nurses, Doctors or other staff. We also classified patients passing through the stages, according to their acuity level and personal characteristics. Then, we defined a function as a measure of the ED performance in respect to the calculated wait times and the cost. Finally, we developed a customized genetic algorithm to find the best performance which reflects the best allocation of service providers into multiple stages of the emergency department.