Evolutionary algorithms have gained popularity as an alternative for dealing with multi-objective optimization problems. However, these algorithms require to perform a relatively high number of fitness function evaluations in order to generate a reasonably good approximation of the Pareto front. This can be a shortcoming when fitness evaluations are computationally expensive. In this paper, we propose an approach that combines an evolutionary algorithm with an ensemble of surrogate models based on support vector machines (SVM), which are used to approximate the fitness functions of a problem. The proposed approach performs a model selection process for determining the appropriate hyperparameters values for each SVM in the ensemble. The ensemble is constructed in an incremental fashion, such that the models are updated with the knowledge gained during the evolutionary process, but the information from previous evaluated regions is also preserved. A criterion based on surrogate fidelity is also proposed for determining when should the surrogates be updated. We evaluate the performance of our proposal using a benchmark of test problems widely used in the literature and we compare our results with respect to those obtained by the NSGA-II. Our proposed approach is able to significantly reduce the number of fitness function evaluations performed, while producing solutions which are close to the true Pareto front.