Most decision-making problems involve more than one objective. The decision about where to set up a new facility is not an exception. This is particularly true when the facility to be located is semi-obnoxious, that is, it is attractive to some of the demand points with which it will interact, and repulsive to others. In this paper we present a new multi-objective facility location model. The first objective is the classical minsum one, where one seeks to minimize the sum of weighted distances from the facility to the demand points that perceive the facility as attractive. The second one is the minimization of the global repulsion of the demand points that consider the facility as obnoxious. The third one (Gini coefficient) is an equity measure, and seeks that the differences among the individual repulsions is minimized. We are interested in obtaining a finite set of points which cover the complete Pareto-front and evenly distributed over it. In this paper we present a general-purpose multi-objective evolutionary algorithm, called FEMOEA, whose aim is to obtain a fix size approximation of the Pareto-front quickly. FEMOEA combines ideas from different multi- and single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to improve the efficiency of points and a new stopping rule to stop the algorithm as soon as a good approximation of the Pareto-front is obtained.