Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach to solve hard optimization problems presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require very little or no prior knowledge of the objective function. Information like differentiability or continuity is not necessary. The inspiration to learn from evolutionary processes and emulate them on a computer comes from varied directions, the most pertinent of which is the field of optimization. This paper presents one such Evolutionary Algorithm known as Differential Evolution (DE) and tests its performance on benchmark problems. Different variants of basic DE are discussed and their advantages and disadvantages are listed. This paper, through exhaustive experimentation, proposes an acceptable set of control parameters which may be applied to most of the benchmark functions to achieve good performance.