The problem of integrated design and control optimization of process plants is discussed in this paper. We consider it as a nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently multimodal and "costly" (i.e., computationally expensive to evaluate). Thus, on the one hand, local optimization techniques usually fail to locate the global solution, and, on the second hand, most global optimization methods require many simulations of the model, resulting in unaffordable computation times. As an alternative, one may consider global optimization methods which employ surrogate-based approaches to reduce computation times and which require no knowledge of the underlying problem structure. A challenging wastewater treatment plant (WWTP) benchmark model(1) is used here to evaluate the performance of these techniques. Numerical experiments with different optimization solvers indicate that the proposed benchmark optimization problem is indeed multimodal, and that via global optimization we can achieve an improvement of the controllers' performance compared to the best tuned controllers' settings available in the literature. Moreover, these results show that surrogate-based methods may reduce computation times while ensuring convergence to the best known solutions.