A fuel cell power plant is a very complex system which has various control loops with some non-linearity. For control of a fuel cell power plant, dynamic models of fuel cell stacks have been developed and simplified process flow diagrams of a fuel cell power plant has been presented. Using such a model of a Molten Carbonate Fuel Cell (MCFC) power plant, this paper deals with development of an intelligent setpoint reference governor (I-SRG) to find the optimal setpoints and feed forward control inputs for the plant power demand. The I-SRG is implemented with neural network by using Particle Swarm Optimization (PSO) algorithm based on system constraints and performance objectives. The feasibility of the I-SRG is shown through simulation of an MCFC power plant for tracking control of its power demand.