In this study, a novel multi-objective (MO) motion compensation (MC) technique is proposed to clear blur effects in the inverse synthetic aperture radar (ISAR) images resulted by the translational motion of the target. Thanks to the MO particle swarm optimization (PSO) algorithm, the velocity and acceleration of the target which minimize the entropy and maximize the contrast of the ISAR image are optimally determined. In order to find an optimal solution from trade-off solutions between entropy and contrast, Pareto front technique is exploited. To demonstrate the performance of the algorithm, the proposed method is implemented for the four ISAR scenarios reported elsewhere, and compared with the single-objective meta-heuristic optimization algorithms (artificial bee colony, genetic algorithm, and PSO with island model) implemented in the literature. Furthermore, the accuracy of the proposed technique is numerically pointed out by comparing the entropy and contrast values of each scenarios with the actual values. The results obviously indicate that the proposed MO MC technique is very successful and efficient compared to single-objective algorithms and performance of the technique is higher than the other methods as the velocity and the acceleration increases.