Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.