A multi-objective gravitational search algorithm(GSA) based on data field(DFMOGSA) is proposed, in which the external archive is applied to store the obtained non-dominated solutions, and is mapped into a data field in the objective space. Accordingly, each non-dominated solution is assigned a density value based on its potential energy. The solution with the smallest density value is chosen as the first kind of guide-particle. This guide-particle directly leads population particles convergence towards the low density region, and thus improves the solution distribution. Moreover, several superior population particles are selected as the second kind of guide-particles. These guide-particles direct each of the population particles fully explore the feasible search space through their resultant gravitational force, which ensures the convergence performance. Simulation results on benchmark test problems show the effectiveness and superiority of the DFMOGSA for multi-objective optimization problems(MOPs).