The purpose of this paper is to show that methods of AI, genetic algorithms in particular, are very effective at solving difficult, important real-world problems, specifically the optimization of serial multiproduct batch plant sequencing, and to present the results of our work in this field. This work deals with the problem of finding a sequence of batches that minimizes the makespan, and discusses the application of different genetic algorithms to find such an optimum sequence. To create such an application, the author must select, create, and modify the appropriate algorithm and set the algorithm's parameters, so that the result is well suited to the specific problem type while remaining flexible enough to be applicable to different problems in the target group. This paper presents the analysis of performance of different algorithm configurations and parameter values and, in addition, proposes a new crossover operator that offers an improvement over older ones. The results obtained by using genetic algorithms are compared to those obtained by using MINLP.