One of the most important energy-saving optimization problems is the Optimal Capacitor Placement (OCP). Essentially, this problem involves the correct allocation of capacitor banks in a bounded Radial Distribution Network (RDN). This is done in order to reduce the power loss of the system and to improve its feeder’s voltage profile. This problem has been addressed by different computational perspectives but the meta-heuristic methods have demonstrated their advantages over other schemes. In that sense, in this work the African Vulture Optimization Algorithm (AVOA) is used to solve the OCP problem due to its efficiency solving other energy-saving tasks. The AVOA is a relatively recent approach that has gained popularity over the years due to its robustness and stability in terms of accuracy and computational resources. In addition, the AVOA is modified with a chaotic Opposition-Based-Learning (OBL) initialization phase to increase the population diversity. The obtained results are compared against other nine classical and contemporary meta-heuristic algorithms for solving two IEEE RDN’ tests. The experimentation shows that the AVOA outperforms the rest of schemes in terms of accuracy and stability. Moreover, statistical and non-parametric tests probes the efficiency of the algorithm and its implication for future research in the field.