Abstract: To address the challenges of inefficient access point (AP) resource allocation and high backhaul load in cell-free massive multi-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems, a joint optimization algorithm of user clustering and AP selection based on binary particle swarm optimization (BPSO) is proposed. The algorithm aims at maximizing the average downlink rate of users by jointly modeling user clustering and AP selection as a binary combinatorial constraint optimization model. An improved BPSO method is employed to achieve efficient solution and effectively coordinate the coupling relationship between clustering and AP selection. Through theoretical analysis and simulation verification, the average rate of system users and AP occupancy rate under different AP selection algorithms are compared. The results show that the proposed algorithm is superior to the comparison algorithms in many indicators. Under imperfect successive interference cancellation with 60 APs, the average downlink rate of users reaches 11.2 Mbit/s, achieving a 10.9% improvement over the quantum bacterial foraging optimization algorithm. The cumulative distribution plot of user average rates shows that the proposed algorithm still maintains peak performance at the 95th percentile, demonstrating outstanding edge user coverage capability. Convergence stabilizes within approximately 30 iterations with superior convergence values compared to benchmark algorithms. Notably, the average AP occupancy rate is reduced by approximately 52.1% compared to full connection schemes, effectively alleviating backhaul link pressure.