In this research the impact of job mix selection in each production shift in a job shop production environment is examined. This is a critical question within photolithography workstations in semiconductor manufacturing systems. For this purpose, a recently developed Simulation Optimization (SO) method named Evolutionary Learning Based Simulation Optimization (ELBSO) is implemented to solve a set of designed Stochastic Job Shop Scheduling problems captured from a real semiconductor manufacturing data set. Experiment results indicate that the best performance in each shift occurs when machines are flexible in terms of processing different job operations, and the selected jobs for a certain shift have as equal as possible due dates.