Since most real-world decision problems, because of incomplete information or the existence of linguistic information in the data, are including uncertainties, stochastic programming and fuzzy programming as two conventional approaches to such issues have been raised. Stochastic programming deals with optimization problems where some or all the parameters are random. In this paper, a method is provided for solving multi-objective stochastic programming where the unknown parameters have been considered as normal random variables. In this model, it is assumed that the parameters are specified by the relevant professionals. Since there are not enough ways to solve such problems directly, the corresponding model using chance-constraint approaches, are converted to a certain multi-objective problem. Then, a fuzzy programming technique for solving the certain multi-objective model will be utilized. In this paper, the hyperbolic membership function is used. The final method can be solved by standard methods of nonlinear programming. Finally, numerical examples are provided to illustrate the operation of the proposed method.