In this paper, we examine the performance of a genetic algorithm based on a Pareto neighborhood search for multiobjective optimization. The purpose of the proposed method is to generate a set of non-dominated solutions that is properly distributed in the neighborhood of the trade-off surface. Simulation results show that the GA based on the proposed method has good performances better than the traditional GA approaches for several multiobjective flowshop scheduling problems. I. INTRODUCTION In practical problems, we often want to optimize more than one measure of performance at once. Multiobjective optimization seeks to optimize the components of a vector-valued cost function. Unlike single objective optimization, the solution to this problem is not a single point, but a family of points known as the Paretooptimal set, also called non-dominated set. Each point in the trade-off surface is optimal in the sense that no improvement can be achieved in one cost vector component that does n..