In this paper, the short-term scheduling optimization of a combined cycle power plant is accomplished by exploiting hybrid systems, i.e., systems evolving according to continuous dynamics, discrete dynamics, and logic rules. Discrete features of a power plant are, for instance, the possibility of turning on/off the turbines, operating constraints like minimum up and down times and the different types of start up of the turbines. On the other hand, features with continuous dynamics are power and steam output, the corresponding fuel consumption, etc. The union of these properties characterize the hybrid behavior of a combined cycle power plant. In order to model both the continuous/discrete dynamics and the switching between different operating conditions, we use the framework of mixed logic dynamical (MLD) systems. Then, we recast the economic optimization problem as a model predictive control (MPC) problem, that allows us to optimize the plant operations by taking into account the time variability of both prices and electricity/steam demands. Because of the presence of integer variables, the MPC scheme is formulated as a mixed integer linear program that can be solved in an efficient way via dedicated software.