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This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct current (DC) grids. To solve it, we propose an optimization approach with an objective function that aims to reduce power losses due to energy transport, while considering all the constraints that represent DC grids in a distributed generation environment. For the mathematical formulation of the problem, we used a mixed-integer nonlinear programming (MINLP) model, which allowed us to evaluate the impact of all possible configurations (i.e., location and size of DGs in the DC network) on the objective function and the constraints. The solution method proposed here is a master–slave strategy that implements a hybrid solution methodology that combines a genetic algorithm (GA) and the vortex search algorithm (VSA). The GA is in charge of solving the location problem in the master stage, and the VSA is responsible for sizing the DGs in the slave stage. To evaluate the effectiveness and robustness of the proposed GA/VSA methodology, we employed two test systems (i.e., 21 and 69 buses) considering a maximum penetration of distributed generation equal to 40% of the power generated by the slack buses. Furthermore, we also implemented nine other hybrid methodologies based on metaheuristic techniques (proposed in the literature for solving the problem addressed here) to make comparisons. All the solution methods used and proposed in this paper are based on sequential programming to avoid the need for specialized software and thus reduce the complexity and cost of the solutions. The effectiveness of the proposed solution was evaluated in two scenarios: (1) peak power demand and (2) variation in power generation and demand associated with photovoltaic generation and user demand in Medellín, Colombia. The results demonstrate that the GA/VSA methodology achieved the best results in terms of solution quality and processing times in all the test scenarios proposed in this study. [ABSTRACT FROM AUTHOR]