This work is devoted to the structural optimization of 5G networks, specifically addressing the problem of base station (BS) placement optimization in indoor network deployment. A method is proposed for determining the number and optimal spatial coordinates of BSs in indoor environments, such as shopping malls or telemedicine centers, under random user distribution to ensure maximum coverage and network throughput while explicitly accounting for intra-system interference. The problem is characterized by dynamic environmental conditions, high user density, heterogeneous service demands, and the requirement for guaranteed network quality indicators, as well as the need to ensure reliable coverage in complex indoor layouts. As a result, the BS placement task is formulated as a nonlinear NP-complete integer programming problem. A genetic algorithm was employed to solve it, incorporating adaptive selection, crossover, and mutation operators. The fitness function was mathematically formulated to maximize the average user data rate while including penalty terms for BS overload, excessive BS proximity, and violations of minimum quality of service (QoS) thresholds. Numerical simulations demonstrate the effectiveness of the proposed approach, confirming that the developed method allows for structural optimization of 5G networks through intelligent base station placement under the influence of intra-system interference.