Summary: Stochastic (or probabilistic) programming is an optimization technique in which the constraints and/or the objective function of an optimization problem contains random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, Differential Evolution (DE) and its two recent variants LDE1 and LDE2 are presented for solving multi objective linear stochastic programming (MOSLP) problems, having several conflicting objectives. The numerical results obtained by DE and its variants are compared with the available results from where it is observed that the DE and its variants significantly improve the quality of solution of the given considered problem in comparison with the quoted results in the literature.