Validation of an analytical method through a series of experiments demonstrates that the method is suitable for its intended purpose. Due to multi-parameters to be examined and a large number of experiments involved in validation, it is important to design the experiments scientifically so that appropriate validation parameters can be examined simultaneously to provide capabilities of the analytical method. This paper describes identifying the value of Genetic Algorithm (GA)'s parameters to get an optimal result and to reduce the cost. We considered two-GA parameters (crossover probability and mutation probability) by using the design of experiment method. Considerations will lead to the success of the optimization and the efficiency of the approaches to other NP-hard problems. A numerical example is provided to illustrate our method.