Fuel cells is a rapidly evolving technology with applications in many industries including transportation, and both portable and stationary power generation. The viability, efficiency and robustness of fuel-cell systems depend strongly on optimization and control of their operation. This paper presents the development of an integrated optimization and control tool for Proton Exchange Membrane Fuel-Cell (PEMFC) systems. Using a detailed simulation model, a database is generated first, which contains steady-state values of the manipulated and controlled variables over the full operational range of the fuel-cell system. In a second step, the database is utilized for producing Radial Basis Function (RBF) neural network “meta-models”. In the third step, a Non-Linear Programming Problem (NLP) is formulated, that takes into account the constraints and limitations of the system and minimizes the consumption of hydrogen, for a given value of power demand. Based on the formulation and solution of the NLP problem, a look-up table is developed, containing the optimal values of the system variables for any possible value of power demand. In the last step, a Model Predictive Control (MPC) methodology is designed, for the optimal control of the system response to successive sep-point changes of power demand. The efficiency of the produced MPC system is illustrated through a number of simulations, which show that a successful dynamic closed-loop behaviour can be achieved, while at the same time the consumption of hydrogen is minimized.