Engineering optimization of a highly nonlinear complex system is always a challenge methodologically and computationally. This is especially true when multistage dynamic optimization is involved. While significant progress has been made in rigorous deterministic algorithms for dynamic optimization, meta-heuristic-based optimization may offer an attractive alternative. This paper introduces a general mathematical framework, called the Population-based Probability Distribution Estimation (PPDE) method, for tackling constrained multistage complex process dynamic optimization problems. Solution identification is accomplished through probability distribution estimation based search in a continuous space, where special solution migration and penalty assignment techniques are integrated. Besides an optimal parameter estimation problem for a reactor system, an automotive coating curing optimization problem is also investigated, where the PPDE successfully minimizes oven energy consumption under various process/product constraints. Optimization results demonstrate superiorities of the method over the Ant Colony System (ACS) based dynamic optimization method. (c) 2007 American Institute of Chemical Engineers.