A multiobjective optimization procedure based on genetic algorithm has been developed to determine optimum operational conditions of polymerization reaction. In this article by using a new selection criterion to choose the next generation members with better quality, optimization efficiency is improved and the number of generations to obtain Pareto optimal set reduced. In this proposed method a novel replacement criterion based on ranking level information and proximity of solutions to the Pareto optimal front is used to choose the next generation members. The polymerization of vinyl acetate has been chosen as an example. Two objective functions, which used in this study, are maximization of the weight average molecular weight up to the desired value and minimization of the residual initiator concentration. A Pareto optimal set of objective functions has been obtained by application of a Pareto set filter operator. Furthermore, the influence of genetic algorithm parameters on the efficiency and convergence of genetic algorithm is studied by changing cross over and mutation probabilities. Because of the flexibility and generality of genetic algorithm, this optimization method is a useful technique with lots of potentials in determination of optimum value of operation parameters.