It is essential for regional power systems to plan reasonably before integrating large-scale and diverse renewable energy sources. However, current research on planning and optimizing the conversion of multiple distributed energy sources and energy sources with multiple objectives faces numerous difficulties. In this study, we introduce a bi-objective planning model that focuses on optimizing both distributed generation and power-to-gas strategies within integrated electricity and natural gas networks. Initially, using constrained programming as a methodological lens, we develop a bi-objective optimal planning model for distributed generation. The primary goals of this model are to minimize the cumulative investment costs of the system and to maximize the integration of renewable energy. The model’s constraints encompass not only limitations on distributed generation investments and various electrical parameters but also those associated with the interplay among uncertainties. Following this, the non-dominated sorting genetic algorithm II is utilized to harmonize the dual objectives: cost reduction and enhancement of renewable energy penetration. This methodology affirms the efficacy of the formulated bi-objective planning model. We findings indicate that equilibrium between economic considerations and renewable energy penetration is achievable, thereby enriching the scope of distributed generation planning.