Searching optimal low-thrust trajectories is usually a difficult and time-consuming task that involves much experience and expert knowledge. This is because the convergence behavior of traditional optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find. Even if the optimizer converges to an optimal trajectory, this is typically close to the initial guess and rarely close to the (unknown) global optimum. Therefore, those methods are called local trajectory optimization methods. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. Furthermore, the performance of the method is assessed for two exemplary interplanetary missions.