There is neuroscientific evidence to suggest that imitation between humans is goal-directed. Therefore, when performing multiple tasks, we internally define an unknown optimal policy to satisfy multiple goals. This work presents a method to transfer a complex behavior composed by a sequence of multiple tasks from a human demonstrator to a humanoid robot. We defined a multi-objective reward function as a measurement of the goal optimality for both human and robot, which is defined in each subtask of the global behavior. We optimize a sequential policy to generate whole-body movements for the robot that produces a reward profile which is compared and matched with the human reward profile, producing an imitative behavior. Furthermore, we can search in the proximity of the solution space to improve the reward profile and innovate a new solution, which is more beneficial for the humanoid. Experiments were carried out in a real humanoid robot.