The ability of Particle Swarm Optimization (PSO) to locate global optimum solutions is combined with the usefulness of Pattern Search Optimization (PS) in finding local optimum values to produce a powerful tool for analyzing aerospace propulsion systems. Two aerospace applications are considered: (1) design a star grain solid rocket motor (SRM) to match specified thrust vs. time curves; and (2) design and optimize a liquid propellant missile system to specified constraints. For the first application, results are compared with those obtained from a “regular” particle swarm optimizer, a binary encoded genetic algorithm (GA) optimizer, and a real code genetic algorithm optimizer. For the second application, results are compared with those obtained from a binary GA. All optimizers are evaluated based on two criteria: (1) “fitness function” accuracy, or how closely solutions meet a specified tolerance, and (2) convergence speed, based on how many calls to the “objective function” are required to meet that tolerance.