Visualization techniques used to visualize the optimization process of multi-objective evolutionary algorithms (MOEAs) have been discussed in the literature, predominantly in the context of aiding domain experts in decision making and in improving the effectiveness of the design optimization process. These techniques provide the decision maker with the ability to directly observe the performance of individual solutions, as well as their distribution in the approximated Pareto-optimal front. In this paper a visualization technique to study the mechanics of a MOEA, as it is solving multi-objective optimization problems (MOOPs), is discussed. The visualization technique uses a scatterplot animation to visualize the evolutionary process of the algorithms search, focusing on the changes in the population of non-dominated solutions obtained for each generation. The ability to visualize the optimization process of the algorithm provides the means to evaluate the performance of the algorithm, as well as visually observing the trade-offs between objectives.