In this study, we develop an elitist multiobjective evolutionary algorithm for approximating the Pareto-optimal frontiers of multiobjective optimization problems. The algorithm converges the true Pareto-optimal frontier while keeping the solutions in the population wellspread over the frontier. Diversity of the solutions is maintained by the territory defining property of the algorithm rather than using an explicit diversity preservation mechanism. This leads to substantial computational efficiency. We test the algorithm on commonly used test problems and compare its performance against well-known benchmark algorithms. In addition to approximating the entire Pareto-optimal frontier, we develop a preference incorporation mechanism to guide the search towards the decision maker’s regions of interest. Based on this mechanism, we implement two variants of the algorithm. The first gathers all preference information before the optimization stage to find approximations of the desired regions. The second one is an interactive algorithm that focuses on the desired region by interacting with the decision maker during the solution process. Based on tests on 2- and 3-objective problems, we observe that both algorithms converge to the preferred regions.