Due to their large variety of applications, complex optimization problems induced a great effort to develop efficient solution techniques, dealing with both continuous and discrete variables involved in nonlinear functions. But among the diversity of those optimization methods, the choice of the relevant technique for the treatment of a given problem keeps being a thorny issue. Within the process engineering context, batch plant design problems provide a good framework to test the performances of various optimization methods: on the one hand, two mathematical programming techniquesDICOPT++ and SBB, implemented in the GAMS environmentand on the other hand, one stochastic method, i.e., a genetic algorithm. Seven examples, showing an increasing complexity, were solved with these three techniques. The resulting comparison enables the evaluation of their efficiency in order to highlight the most appropriate method for a given problem instance. It was proved that the best performing method is SBB, even if the genetic algorithm (GA) also provides interesting solutions, in terms of quality as well as of computational time.