This study aims to compare Particle Swarm Optimization (PSO) and Differential Evolution (DE) methods for various input parameters. Both optimization methods show high performance in optimization of any physical system including simple and complex constraints and objectives. Average and standard values of both methods were evaluated by utilizing 8 benchmark functions and a graphical representation and comparison of corresponding methods was presented for 50x50 and 100x100 population sizes and dimensionalities. It is concluded that DE and PSO show the best fitness value for Sum of Different Powers benchmark function for both number of populations. Approach to the optimum is found to be faster through the PSO method. Both methods are flexible to be used for simple and complex engineering problems with high performances with ease of programming.