We describe a tailored strategy for nonlinear programming (NLP) with partial differential equation (PDE) models. This approach is based on a reduced space Successive Quadratic Programming (rSQP) algorithm, and it allows the reuse of existing PDE-based modeling codes. This approach leads to an efficient simultaneous strategy, where the NLP and the PDE model are solved at the same time. A number of refinements were made to the NLP algorithm to enhance its performance and reliability for large, nonlinear models. This study considers PDE-based optimization problems to demonstrate the effectiveness of this approach. In particular, we apply this approach to MPSalsa, a finite element PDE Solver developed at Sandia National Laboratories, and consider a chemical vapor deposition (CVD) reactor model as the optimization application. Here, our goal is optimize the operating conditions to produce a thin film of gallium nitride with a spatially uniform thickness. To minimize nonuniformity of the film, a novel NLP formulation is described and evaluated based on constraint aggregation. In addition to the design optimization strategy, we incorporate a stability analysis of the optimal design to assure that it is a stable steady state. (C) 2003 Elsevier Ltd. All rights reserved.