This second part of this series of papers deals with the second stage of the methodology developed for multiobjective batch plant design, that is, the implementation of a dedicated genetic algorithm. This procedure can be viewed as the search engine for workshop configurations and uses the simulation model as a subroutine for evaluating the generated structure feasibility. First, the basic principles of genetic algorithms (GAs) are briefly recalled; second, the GA, especially developed for treating batch plant design, is largely presented. An interesting concept has been introduced, that is, the so-called "gene fridge" to prevent population degradation by allowing the introduction of previous genes during evolution. Besides, this procedure allows a better screening of search space and the evaluation of the problem combinatorics. Finally, this concept drives the search toward critical steps, by introducing a varying mutation probability function of the gene locus. An illustrative example, already presented in part 1 of this series, is largely analyzed and provides useful guidelines for treating similar problems. The parametric study is particularly interesting for GA parameter setting, which constitutes a key problem.