The success of local search techniques in the solution of combinatorial optimization problems has motivated their incorporation into multi-objective evolutionary algorithms, giving rise to the so-called multi-objective memetic algorithms (MOMAs). The main advantage for adopting this sort of hybridization is to speed up convergence to the Pareto front. However, the use of MOMAs introduces new issues, such as how to select the solutions to which the local search will be applied and for how long to run the local search engine (the use of such a local search engine has an extra computational cost). Here, we propose a new MOMA which switches between a hypervolume-based global optimizer and an IGD+-based local search engine. Our proposed local search engine adopts a novel clustering technique based on the IGD+ indicator for splitting the objective space into sub-regions. Since both computing the hypervolume and applying a local search engine are very costly procedures, we propose a GPU-based parallelization of our algorithm. Our preliminary results indicate that our MOMA is able to converge faster than SMS-EMOA to the true Pareto front of multi-objective problems having different degrees of difficulty.