Addressing the challenging issue of error rate performance analysis and optimization in non-coherent massive single-input multiple-output (SIMO） systems, a differential amplitude phase shift keying (DAPSK） constellation design scheme based on evolutionary computation is proposed. Initially, the constellation structure is initialized according to the user modulation order and DAPSK modulation characteristics. Subsequently, an evolutionary computation framework is introduced for iterative optimization. During the evolutionary process, the initial constellation distribution is adjusted by maximizing the minimum Euclidean distance between joint constellation points, thereby reducing the computational complexity of subsequent searches. The amplitude and phase of user symbols are used as chromosome encoding parameters. Under the constraints of total power and user sub-constellation sets, the bit error rate (BER） obtained from Monte Carlo simulations is minimized as the fitness function. Candidate schemes are generated through selection, crossover and mutation operations, and the population size and evolutionary generations are dynamically adjusted to enhance convergence efficiency, ultimately obtaining the optimal constellation design scheme. Simulation results demonstrate that, compared to the baseline scheme, the proposed scheme achieves a system BER reduction of approximately 1 to 2 orders of magnitude under medium to high signal-to-noise ratio conditions and with large-scale antenna configurations. The research results demonstrate that the proposed scheme not only improve system performance but also effectively reduce detection complexity, providing a feasible solution for ultra-reliable low-latency industrial internet of things communication.