The rising demand for electric vehicles (EVs) requires robust and accessible charging infrastructure, particularly in developing countries where frequent electricity disruptions pose a significant service-reliability risk. Prior charging-infrastructure optimization studies rarely address such disruptions rigorously, yielding suboptimal and fragile designs. We present a simulation-based optimization model that estimates each candidate station's service reliability and embeds it directly in the objective and constraints, framing charging-network design as an engineering-management decision under uncertainty. To make the approach computationally tractable, we apply the control variates (CV) variance-reduction technique to the reliability estimation. Using a real dataset from Surabaya, Indonesia, our numerical experiment shows that the proposed robust model achieves a 13% higher average objective value than the non-robust benchmark and remains robust even when station reliability is under- or over-estimated. The CV technique reduces the required simulation sample size by up to a factor of 10 relative to Monte Carlo, allowing the model to be solved efficiently with a standard mixed-integer programming (MIP) solver.

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