An integrated intelligent algorithm is proposed to optimize the reliability, maintainability, and total cost in the job shop production system. The algorithm consists of three basic modules of computer simulation. each comprising three phases of Algorithm, simulation, and Experiments/robustness validation. In the design phase, different scenarios are determined by changing parameters affecting the reliability, maintainability, and total cost. The job shop production system is simulated in the simulation phase. Then, a fuzzy simulation approach is implemented to run the simulation model for each scenario with ambiguous inputs. Accordingly, the investment cost, maintenance cost, mean time to repair (MTTR), and mean time to failure (MTTF) are obtained. Finally, the performance of different scenarios is assessed in the third module. ANN and DEA are separately used in this module and the preferred method is selected based on the robustness test and extensive sensitivity analysis. DEA and ANN are then employed to rank the design alternatives concerning the initial inputs and outputs. To show the applicability and superiority of the proposed integrated algorithm, it is applied to optimize the design of a fuzzy job shop production system consisting of five workstations.