In modern power distribution systems, the integration of distributed generation (DG) projects is crucial for enhancing system efficiency, safety, and sustainability. However, the optimal selection of DG projects amid various technical, economic, and operational constraints remains challenging. Previous research has focused on optimal placement and sizing of DG, but the realities faced by distribution system operators (DSOs) often involve evaluating predetermined project proposals. This paper introduces a novel approach for the optimal selection of DG projects in power distribution systems using a genetic algorithm (GA) framework integrated with DIgSILENT PowerFactory. The proposed methodology employs a GA to maximize system performance by considering factors such as voltage regulation, line loadability, power losses, and the direction of power flow while adhering to system constraints. The integration with DIgSILENT PowerFactory enables realistic simulation through unbalanced power flow analysis and evaluation of candidate DG projects within the distribution system context. The approach is implemented using DIgSILENT’s built-in programming language, facilitating direct utilization of utility databases and intricate modeling of power system elements. A case study on a real Colombian utility’s distribution feeder demonstrates the effectiveness of the proposed approach. The method achieved a 70.79% reduction in power losses, a 66.57% decrease in maximum line loadability, and voltage profile improvements of up to 7.5% at critical buses, while ensuring no reverse power flow towards the substation. This research contributes to advancing power system optimization by providing DSOs with a practical tool for assessing and selecting DG projects that enhance system performance while mitigating potential negative impacts. The integration with DIgSILENT PowerFactory ensures applicability in real-world scenarios. Future work may explore incorporating additional objectives such as reliability indices and economic factors, and revisiting the constraint on reverse power flow as distribution systems evolve.