When controlling an autonomous system, it is inefficient or sometimes impossible for the human operator to specify detailed commands. Instead, the field of AI autonomy has developed goal-directed systems, in which human operators specify a series of goals to be accomplished. Increasingly, the control of autonomous systems involves performing a mix of discrete and continuous actions. For example, a typical autonomous underwater vehicle (AUV) mission involves discrete actions, like get GPS and set sonar, and continuous actions, like descend and ascend, which involve continuous dynamics of the vehicle. Accordingly, we develop a hybrid planner that determines a series of discrete and continuous actions that achieve the mission goals. In this paper, we describe a novel approach to solving the generative planning problem for hybrid systems, involving both continuous and discrete actions. The planner, Kongming, incorporates two innovations. First, it employs a compact representation of all hybrid plans, called a Hybrid Flow Graph, which combines the strengths of a Planning Graph for discrete actions and Flow Tubes for continuous actions. Second, it encodes the Hybrid Flow Graph as a mixed logic linear/nonlinear program, which it solves using an off-the-shelf solver. We empirically demonstrate that Kongming can efficiently plan for real-world scenarios that are based on science missions performed at the Monterey Bay Aquarium Research Institute (MBARI).