Nowadays, the most real-life problems are multi-objective or many objective in nature, which needs to be optimized to give a promising solution to the user. But the problem comes when we have to select the most significant solution from the large solution space. As a result, by applying genetic algorithm, we get a front which contains number of optimal solutions named as Pareto-optimal front. To select the most significant solution from a number of optimal solutions is a very difficult task as all solutions are nondominated to each other. The decision maker has to select a single solution. In this paper, we have shown an improvement on NSGA-II in order to select quickly the most significant and acceptable solution by the decision maker.