Distribution network operators (DNOs) share a significant responsibility regarding the assurance of electrical energy supply quality and continuity. In detail, DNOs are legally required to: (i) address electrical emergency occurrences quickly, especially to restore electricity supply, and; (ii) ensure the efficiency of service concerning commercial occurrences. In this work, we propose a solution to optimize the logistics operations of the Spanish multinational electric utility company Iberdrola. Our work scope is Neoenergia, the Brazilian subsidiary controlling five different DNOs. In our work, we follow the CRISP-DM data science framework to address the allocation of operations bases. The solution was developed and successfully deployed in collaboration with the analytics team of Neoenergia. In detail, we model the problem as a knapsack and tackle it with an iterated greedy metaheuristic. Results show a decrease in the distances between bases and occurrences when compared to the current approach adopted by Neoenergia. Our approach also reduces travel times, contributes to the improvement of supply continuity indices, and better meets company business requirements. Importantly, we provide a simulation tool to recommend future base allocation, which comprises valuable input to planning.