One of the major applications of fuel cells is as onsite stationary electric power plants. Several types of configurations have been hypothesized and tested for these kinds of applications at the conceptual level but hybrid power plants are one of the most efficient. These are designs that combine a fuel cell cycle with other thermodynamic cycles to provide higher efficiency. Generally, the heat rejected by the fuel cell at a higher temperature is used in a bottoming cycle to generate steam. In this work we are considering a conceptual design of a solid oxide fuel cell-proton exchange membrane (SOFC-PEM) fuel cell hybrid power plant [R. Geisbrecht, Compact Electrochemical Reformer Based on SOFC Technology, AIChE Spring National Meeting, Atlanta, GA, 2000] in which the high temperature SOFC fuel cell acts both as electricity producer and fuel reformer for the low temperature PEM fuel cell (PEMFC). The exhaust from the PEM fuel cell goes to a waste hydrogen burner and heat recovery steam generator that produces steam for further utilizations. Optimizing this conceptual design involves consideration of a number of objectives. The process should have low pollutant emissions as well as cost competitive with the existing technology. The solution of a multi-objective optimization problem is not a single solution but a complete non-dominated or Pareto set, which includes the alternatives representing potential compromise solutions among the objectives. This makes a range of choice available to decision makers and provides them with the trade-off information among the multiple objectives effectively. This paper presents the optimal trade-off design solutions or the Pareto set for this hybrid power plant through a multi-objective optimization framework. This hybrid technology is new and the system level models used for fuel cells performance have significant uncertainties in them. In this paper, we characterize these uncertainties and study the effect of these uncertainties on the optimal trade-offs. The framework developed in this work forms the basis for optimal design and synthesis of any power plant under uncertainties in the face of multiple objectives. (C) 2004 Elsevier B.V. All rights reserved.