Hyper-heuristics are high-level methodologies responsible for automatically discover how to combine elements from a low-level heuristic set in order to solve optimization problems. Agents, in turn, are autonomous component responsible for watching an environment and perform some actions according to their perceptions. Thus, agent-based techniques seem suitable for the design of hyper-heuristics. This work presents an agent-based hyper-heuristic framework for choosing the best low-level heuristic. The proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which multi-objective evolutionary algorithm (MOEA) should generate more new solutions along the execution.