The problem of simultaneous design and control optimization of nonlinear process plants is discussed in this contribution. We consider the formulation as a nonlinear programming problem subject to differential algebraic constraints. This class of problems is frequently highly multimodal, so multistart techniques might fail to locate good solutions even after extensive computation. As an alternative, we consider the use of several global optimization solvers which require no knowledge of the problem structure. It is shown how the problem can be efficiently solved using several stochastic global optimization methods. In particular, we consider a challenging case study related to the integrated process design and control of a wastewater treatment plant. A simple scheme for the rapid development of parallel versions of the stochastic solvers is also presented. These parallel versions can run on available networks of computers (clusters), allowing the solution of this and larger problems in modest computation times.