The integration of distributed generation (DG) sources, such as photovoltaic (PV) systems, into electrical power networks presents significant challenges and opportunities. With the increasing penetration of renewable energy sources, optimizing their placement and operation becomes crucial to ensure the reliability, efficiency, and economic viability of power systems. This study presents a master-slave methodology for optimally integrating and operating photovoltaic (PV) generators using multi-objective optimization. This methodology can simultaneously improve technical and economic aspects of the network by determining the best locations and power injection levels for distributed generation sources. Its master stage uses one out of three different algorithms-Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), or the Multi-Objective Ant Lion Optimizer (MOALO)-while the slave stage is always performed by a load flow analyzer. The three algorithms in the master stage were implemented considering variable generation and demand conditions in 33 and 27 bus feeders, representing urban and rural areas respectively. The results demonstrated the effectiveness of these algorithms. NSGA-II achieved the best performance, with reductions of 32.84% in energy losses and 42.41% in operating costs (with standard deviations of 0.21% and 0.39%, respectively) for the urban system; and reductions of 21.87% in energy losses and 43.36% in operating costs (with standard deviations of 0.07% and 0.24%, respectively) for the rural system. All of this was achieved within short solution processing times during a typical day in the proposed test scenarios.