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In wireless sensor networks, time division multiple access (TDMA)-based MAC can eliminate collisions, hence save energy and guarantee a bounded delay. However, the slot scheduling problem in TDMA is an NP problem. To minimized the total slots needed by a set of data collection tasks and saving the energy consumed on switching between the active and sleep states, a multi-objective TDMA scheduling scheme needs to be achieved. Nevertheless, owing to the high computational complexity, it is quite difficult to achieve an optimal solution. In this paper, a new hybrid algorithm(HPSO), particle swarm optimization (PSO) embedded with simulated annealing (SA), is proposed against such TDMA scheduling. It combines the high search efficiency and strong global search ability of PSO with good local search ability of SA, thus greatly improving time slot allocation in wireless sensor networks. Simulation results validate that HPSO outperforms three other algorithms in the literature.