This paper explores the evolution of strategies for teams participating in track cycling. The performance of a team depends on their strategy. Evolutionary algorithms can help find good strategies to help teams improve their times. However, questions arise about the robustness of the strategies. Strategies that lead to good times may be more fragile, leading to huge changes in performance if they are not followed perfectly. Strategies exhibiting good performance and robustness may be more desirable. We explore the robustness of evolved strategies and present results for different team configurations.