A modern space mission is usually composed of several events such as impulsive maneuvers, thrust arcs, and flybys. Traditionally, a mission planner would develop a structure for the mission using categorical variables, and then find the best spacecraft trajectory solving a continuous optimal control problem. A problem of this type involving categorical and continuous variables in the formulation is known as a hybrid optimal control (HOC) problem. A recent approach to solving HOC problems has the potential to automate the mission planning process by minimizing human intervention in the loop. The method uses two nested loops: an outer-loop which handles the finite dynamics and finds a solution sequence in terms of the categorical variables, and an inner-loop which performs the optimization of the continuous-time dynamical system and obtains the required control law. In this work, we introduce genetic algorithms and Runge-Kutta parallel-shooting with nonlinear programming as methods of solution for the outer-loop and inner-loop problems respectively.