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Routine inspection programs are procedures that utilities can use to improve their power quality standards and public perception. However, due to budget restrictions, it is usually not possible to inspect all elements in the whole concession area. In this scenario, strategies to efficiently allocate available teams at scheduled inspections are an interesting alternative. The challenge is to define which data are relevant and at hand to develop an strategy that is comprehensive, efficient and outperforms the current old fashioned way based on personal experience applied to automated spreadsheets. This paper proposes an approach that deals with a large amount of data from corporate systems as input to a proposed optimization engine based on Genetic Algorithm and heuristics. Primary feeders are split into segments among protection devices, referred to as metazones. The developed tool produces a set of prioritized metazones by maximizing the sum of merit indexes related to loss of energy, voltage and current violations, as well as outages, while satisfying teams’ inspections capacities. A case study with 56 primary feeders is presented, and results show that the yielded prioritized metazones are compatible with and improve current power utilities practices. [ABSTRACT FROM AUTHOR]