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This paper presents how genetic algorithm (GA) can be used in fuzzy goal programming (FGP) formulation of multiobjective stochastic programming (SP) problems. In the proposed approach, the individual optimal decision of each of the objectives are determined by using the GA scheme adopted in the process of solving the problem after converting the chance constraints into their deterministic equivalent in. Then, the FGP model of the problem is formulated by introducing the concept of tolerance membership functions in fuzzy sets. In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. Two numerical examples are solved to illustrate the approach. The model solution of the first example is compared with the solution of the conventional approach studied previously.