Particle Swarm Optimization(PSO) is an Evolutionary Computation(EC) approach for solving optimization problems in engineering. This research aims to investigate the effect of implementing various cost function penalty strategies on algorithm performance. By adjusting the penalty strategies, the performance of the PSO algorithm can be modified through the effect of penalizing non-feasible solutions. In this work, the Hohmann Transfer was the test problem, and both optimized and unoptimized versions of the PSO algorithm are tested.