The evolution in the field of actuators has led to very powerful and smart drives, combining mechanics with electronic and informatics, which require a suitable control to exploit potential advantages. A common requirement for actuators is time optimality, and thus it is sensible to look for time optimal control strategies. For known systems time optimal control can be formulated using standard methods. Unfortunately, in rare cases the problem is convex and thus requires the use of numerical methods, which may lead to local optima. Against this background, this work, started in the framework of an industrial project, presents a method to obtain an approximation of the time optimal control by iterative learning. The method itself consist of two learning loops, one based on the convergence of learning iterations enforcing the tracking of a specified trajectory, the other one adapting the trajectory to obtain a time (sub)optimal solution. To evaluate the me-thod, two examples are presented a compressor valve actuator and the steepest ascent of an aircraft.