This chapter first explains the motivation for the application of genetic algorithms (GA) in job sequencing and scheduling. Then the evolution process of GA is introduced, especially the steps. Several common techniques and tools for explaining or visualizing these steps are also reviewed. Clearly, such GA applications may remain difficult to understand and communicate due to their stochastic nature. Explainable artificial intelligence (XAI) can play a role in solving this difficulty. The problems faced by existing XAI techniques in interpreting GA applications in job sequencing and scheduling are also discussed. To address these issues, XAI new techniques and tools have emerged. These techniques and tools fall into two categories: textual descriptions and visualization. Four visualization techniques for explaining the application of GA in job sequencing and scheduling are introduced, including generic visualization techniques, saliency diagrams, decision tree-based interpretation, dynamic transition and contribution diagrams and Shapley value (SHAP) analysis.