Direction of arrival (DOA) is widely used in communication, biomedicine, and other fields. Stochastic maximum likelihood (SML) algorithm is an excellent direction of arrival (DOA) estimation algorithm. However, the extremely heavy computational complexity in the process of SML analysis restricts its application in practical systems. Aiming at this problem of SML, this paper proposes a parallel accelerating algorithm of membrane computing (MC), particle swarm optimization (PSO), and artificial bee colony (ABC). Firstly, the solution space of SML algorithm is divided into basic membrane and surface membrane by using membrane computing; then particle swarm optimization algorithm is used for local parallel optimization in each basic membrane, and the locally optimal solution is transferred to the surface membrane; finally, the artificial bee colony algorithm is used to find the global optimum in the surface membrane. The results of the experiment show that the proposed algorithm greatly reduces the analytical complexity of SML, and the calculation time is decreased by more than 5 times compared with the commonly used optimization algorithms such as GA, AM, and PSO.