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With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum completion time and minimum energy consumption. Second, for the shortcomings of the chicken swarm optimization algorithm that easily fall into local optimums leading to algorithm stagnation, we use reverse learning initialization for the chicken flock population to expand the space of understanding and an adaptive strategy for learning factors and following coefficients. To illustrate the effectiveness of our algorithm in scheduling, we chose the number of mobile devices as 50, 100, and 150 and compared the improved chicken swarm optimization algorithm, ant colony algorithm, particle swarm algorithm, and chicken swarm optimization algorithm. The results illustrate that our proposed algorithm can reduce the task completion time, control the energy consumption of mobile devices well, and save energy. &nbsp