Multi-objective optimization involves optimizing a number of objectives simultaneously, and it becomes challenging when the objectives conflict each other, i.e., the optimal solution of one objective function is different from that of other. These problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solution. Due to multiplicity in solutions, these problems were proposed to be solved suitably by using evolutionary algorithms which use a population approach in search procedure. So, these types of problems are called evolutionary multi-objective optimization (EMO) for handling multi-objective optimization problems. In this paper, an average-point-based EMO algorithm has been suggested for solving multi-objective optimization problem following NSGA-II mechanism (AP-NSGA-II) that emphasizes population members that are non-dominated. Finally, it has been shown how our two primary goals, convergence to Paretooptimal solution and maintenance of diversity among solutions, have been achieved. © Springer India 2015.