Since practical problems often are very complex with a large number of objectives, it can be difficult or impossible to create an objective function expressing all the criteria of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting. Because evolutionary algorithms are population based, they have the best potential for finding more of the best solutions among the possible solutions. However, standard EAs often converge to one solution and leave therefore only this single option for a final human selection. So far, at least two methods, sharing and tagging, have been proposed to solve the problem. The paper presents a new method for finding more quality solutions, not only global optimas but local as well. The method tries to adapt its search strategy to the problem by taking the topology of the fitness landscape into account. The idea is to use the topology to group the individuals into sub-populations, each covering a part of the fitness landscape.