In recent decades, a novel class of optimization techniques, namely metaheuristics, has been developed and devoted to the solution of highly combinatorial discrete problems.The improvements provided by these methods were extended to the continuous or mixed-integer optimization area. This chapter addresses the problem of adapting a Genetic Algorithm (GA) to a Mixed Integer Non-linear Programming (MINLP) problem.The basis of the work is optimal batch plant design, which is of great interest in the framework of Process Engineering. This study deals with the two main issues for GAs, i.e. the treatment of continuous variables by specific encoding and efficient constraints handling in GA. Various techniques are tested for both topics and numerical results show that the use of a mixed real-discrete encoding and a specific domination-based tournament method is the most appropriate approach.