Hyper-Heuristics is a high-level methodology for selection or generation of heuristics for solving complex problems. Despite their success, there is a lack of multi-objective hyper-heuristics. Our approach, named MOEA/D-HH, is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. MOEA/D decomposes a multiobjective optimization problem into a number of subproblems, where each subproblem is handled by an agent in a collaborative manner. MOEA/D-HH uses an adaptive choice function with sliding window proposed in this work to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied by each agent during a MOEA/D execution. MOEA/D-HH was tested in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HH was favourably compared with state-of-the-art multi-objective optimization algorithms.