Combined plant/control optimization is applied to a PEM hybrid fuel cell vehicle (HFCV) for vehicle to grid (V2G) applications. The HFCV model is developed from past control-oriented models. For the purposes of design optimization, three components (fuel cell stack, compressor, and battery) are made scalable. To construct a control scheme suitable for combined plant/ control design optimization, a rule-based method is selected and framed in a manner such that several key parameters are formulated as design variables. Simulation based computations of the objective function are characterized by noise, and therefore inappropriate for gradient-based optimization algorithms. A surrogate modeling method is suggested using neural networks to approximate the physical model. Using the surrogate model, the combined design and controller HFCV model is optimized for maximum fuel economy for a given stationary power demand cycle. The solution is analyzed with respect to various optimality properties, such as constraint activity, Lagrange multipliers, interior & bounded solutions, and varying starting points. The trade-offs between optimal design solutions and constraints is observed and analyzed to analyze optimal design solutions for a PEM HFCV operating as an energy source to the power grid. Multi-objective optimization problems are formulated through parametric studies to elucidate trade offs between different design objectives. A resultant set of "design rules" are formulated to provide a physical engineering interpretation of the conclusions found.