Most real world problems are multiobjective by nature and expensive to evaluate. We propose in this work a surrogate assisted approach for multiobjective evolutionary algorithms by building a surrogate model on each objective. However, integrating a surrogate model within an optimization process generates complexity with additional hyper-parameters to tune. Empirical validation on standards MOO benchmark problems of a use case based on NSGA-II and surrogates using SVM regression shows a significant improvement of the optimization cost in terms of true objectives evaluations, especially for low budget. We also discuss the behavior of the proposed algorithm using different values of the parameter calibrating the use of the surrogate model.