About two decades ago years researchers began to apply a new approach, using evolutionary algorithms or metaheuristics, to solve continuous optimal control problems. The evolutionary algorithms use the principle of “survival of the fittest” applied to a population of individuals representing candidate solutions for the optimal trajectories. Metaheuristics optimize by iteratively acting to improve candidate solutions, often using stochastic methods. Because of certain compromises that are usually necessary when transcribing the problem for solution by these methods it has been thought that they were not capable of yielding accurate solutions. However that is a misconception as is demonstrated by examples in this work.