The problem of integrated process and control system design is discussed in this paper. We formulate it as a mixed integer nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently nonconvex and, therefore, local optimization techniques usually fail to locate the global solution. Here we propose a global optimization algorithm, based oil an extension of the ant colony optimization metaheuristic, in order to solve this challenging class of problems in,in efficient and robust way. The ideas of the methodology are explained and, oil the basis of different full-plant case studies, the performance of the approach is evaluated. The first set of benchmark problems deal with the integrated design and control of two different wastewater treatment plants, consisting on both NLP and MINLP formulations. The last case Study is the well-known Tennessee Eastman process. Numerical experiments with our new method indicate that we can achieve all improved performance in all cases. Additionally, Our method outperforms several other recent competitive solvers for the challenging case Studies considered.