Generative Adversarial Network (GAN) is a family of machine learning algorithms designed to train neural networks able to imitate real data distributions. Unfortunately, GAN suffers from problems such as gradient vanishing and mode collapse. In Multi-Objective Evolutionary Generative Adversarial Network (MO-EGAN) these problems were addressed using an evolutionary technique combined with Multi-Objective selection, obtaining better results on synthetic datasets at the expense of larger computation times. In this works, we present the Smart MultiObjective Evolutionary Generative Adversarial Network (SMO-EGAN) algorithm, which reduces the computational cost of MO-EGAN and achieves better results on real data distributions.