Integrating machine learning (ML) techniques into metaheuristics is an efficient approach in single-objective optimization. Indeed, high-quality solutions often contain relevant knowledge, that can be used to guide the heuristic towards promising areas. In multiobjective optimization, the quality of solutions is evaluated according to multiple criteria that are generally conflicting. Therefore, the ML techniques designed for single-objective optimization can not be directly adapted for multi-objective optimization. In this paper, we propose to enhance the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with a clustering-based learning mechanism. To be more precise, solutions are grouped regarding a metric based on their quality on each criterion, and the knowledge from the solutions of the same group is merged. Experiments are conducted on the multi-objective vehicle routing problem with time windows. The results show that MOEA/D with learning outperforms the original version.