Design methodologies of today require the solution of several many-objective optimization problems. The last two decades have seen a surge in several algorithms capable of solving multi-objective optimization problems. It was only in the past 5 years that new algorithms capable of coping with a large number of objectives have been introduced. This work presents a new differential evolution algorithm (NSDE-R) capable of efficiently solving many-objective optimization problems. The algorithms make use of reference points evenly distributed through the objective function space to preserve diversity and aid in multi-criteria-decision-making. The proposed NSDE-R was applied to test problems from the DTLZ and WFG suite, having three to 15 objectives. Two mutation donor operators were investigated for their ability to converge to the analytical Pareto front while maintaining diversity. The ability of NSDE-R to converge to a user-specified region of the Pareto front is also investigated. The proposed NSDE-R algorithm has shown to have a higher rate of convergence and better convergence to the analytical Pareto front.