Solving multi-objective scientific and engineering problems is, generally, a very difficult goal. In these optimization problems, the objectives often conflict across a high-dimensional problem space and require extensive computational resources. In this paper, a migration model of parallelization is developed for a genetic algorithm (GA) based multi-objective evolutionary algorithm (MOEA). The MOEA generates a near-optimal schedule by simultaneously achieving two contradicting objectives of a flexible manufacturing system (FMS). The parallel implementation of the migration model showed a speedup in computation time and needed less objective function evaluations when compared to a single-population algorithm. So, even for a single-processor computer, implementing the parallel algorithm in a serial manner (pseudo-parallel) delivers better results. Two versions of the migration model are constructed and the performance of two parallel GAs is compared for their effectiveness in bringing genetic diversity and minimizing the total number of functional evaluations.