The effect of selection pressure on evolution in centralized evolutionary algorithms (EA’s) is relatively well understood. Selection pressure pushes evolution toward better performing individuals. However, distributed EA’s in an Evolutionary Robotics (ER) context differ in that the population is distributed across the agents, and a global vision of all the individuals is not available. In this paper, we analyze the influence of selection pressure in such a distributed context. We propose a version of mEDEA that adds a selection pressure, and evaluate its effect on two multi-robot tasks: navigation and obstacle avoidance, and collective foraging. Experiments show that even small intensities of selection pressure lead to good performances, and that performance increases with selection pressure. This is opposed to the lower selection pressure that is usually preferred in centralized approaches to avoid stagnating in local optima.