Scheduling, in general, has been defined as the allocation of the resources to the jobs in order to ensure the termination of these jobs in achieving the required objective. The flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem aiming to improve on-time delivery and utilization of bottleneck resources, cut lead times and reduce inventory. Due to the combinatorial nature of FJSP, to find optimal schedules within a limited computation time is often difficult. In classical scheduling problems, processing times are defined as certain times. In this paper, because of the nature of manufacturing environment, processing times are modeled as fuzzy numbers to handle uncertainty and vagueness. For obtaining feasible solutions under fuzzy environment a fuzzified algorithm is presented and to find optimal solutions a modified Pareto Simulated Annealing Approach is applied for the predefined multi objectives. A case study is performed and results are discussed.