This paper presents the importance of parameters tuning in global optimization algorithms. The primary objective of an experiment is to recognize the process. The experiments are carried out to learn the effect of various factors at different levels. Hence, identifying the optimal parameters setting is important for robust design. One of the most popular global optimization algorithms: genetic algorithm is considered in this study. The domain of inquiry is travelling salesman problem. The present study employs the Taguchi method that involves the use of an orthogonal array in the estimation of the factors. Taguchi approach has been widely applied in experimental design for problems with multiple factors. The use of Taguchi design is a novel idea—leads to efficient algorithms—can find a satisfactory solution in a few iterations, which improves the convergence speed and reduces the cost. Experimental results show that the Taguchi design is less sensitive to initial value of parameters. Two versions of genetic algorithms (with tuning and without tuning) are implemented. The analysis shows the superiority of genetic algorithm with tuning over genetic algorithm without tuning.