Many scientific, engineering and economic problems involve the optimization of a set of parameters. The Particle Swarm Optimization (PSO) is one of the new techniques that have been empirically shown to perform well. The PSO algorithm is a population-based search algorithm based on simulating the social behavior of birds within a flock. Large-scale engineering optimization problems impose large computational demands, resulting in long solution times even on modern high-end processors. To obtain enhanced computational throughput and global search capability parallel algorithms and parallel architectures have drawn lots of attention. Parallelization of PSO has proved to enhance computational throughput and global search capability In this paper, we detail the parallelization of an increasingly popular global search method, the PSO algorithm using MPJ Express. Both synchronous and asynchronous parallel implementations are investigated. The parallel PSO algorithm’s robustness and efficiency are demonstrated by using four standard benchmark functions Alpine, Rosenbrock, Rastrigin and Schaffer.