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Many algorithms have been developed to optimize police patrol services. Previous studies have mainly focused on determining the important locations (e.g., crime hotspots) and identifying important routes based on the topology of road networks. However, the impact of the patterns of hotspots on patrol route selections and the collective performance of patrol activities were rarely considered in these studies. In addition, some algorithms lack a mechanism to ensure the randomness in patrolling and some are not efficient enough for real-time applications. In this study, we propose an approach to determine sets of patrol routes, which can help to optimize the collective efforts of multiple patrol activity. In our approach, we integrate the Getis-Ord Gi* with the Cross Entropy approach to produce randomized optimal patrol routes. We also address how to ensure the randomness in the route identification using the Cross Entropy approach. Our results indicate that our approach can help to improve the collective performance of police patrol service and it is also efficient enough for real-time application.