In this paper, we propose a new selection mechanism for Multi-Objective Evolutionary Algorithms (MOEAs), which is based on the generational distance indicator and uses a technique that relies on Euclidean distances to maintain diversity in the population (in objective function space). Our proposed selecion mechanism is incorporated into a MOEA which adopts the operators of NSGA-II (crossover and mutation) to generate new individuals. The newMOEA is called "Generational Distance -Multi-Objective Evolutionary Algorithm (GD-MOEA). "Our GD-MOEA is validated using standard test problems taken from the specialized literature, having three to six objective functions. GD-MOEA is compared with respect toMOEA/D using Penalty Boundary Intersection (PBI), which is based on decomposition, and to SMS- EMOA-HYPE (a version of SMS-EMOA that uses a fitness assignment scheme based on the use of an approximation of the hypervolume indicator). Our preliminary results indicate that if we consider both quality in the solutions and the running time required to generate them, our GD-MOEA is a good alternative to solve multi-objective optimization problems having both low dimensionality and high dimensionality in objective function space.