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In order to mitigate the hot spot problem and prolong the network lifetime, data gathering with mobile sink is an effective measure to enhance the system performance. However, the movement strategy of sink node can be regarded as traveling salesman problem, which can hardly obtain the solution with polynomial running time. To address above problem, an energy-aware data gathering mechanism for mobile sink in wireless sensor networks using particle swarm optimization is introduced. Firstly, the mathematical model is established according to the total energy consumption and delay constraints for mobile sink's data collection. Then, the optimal rendezvous points are selected to aggregate data originated from the source nodes through multi-hop relay, and the aggregation tree will be constructed for data transmission. The spanning tree is encoded into particles, and the random method is designed to generate the data collection spanning tree with constrain of tree height limit. Furthermore, a particle swarm optimization strategy with adaptive elite mutation is designed to improve the population diversity and avoid falling into the local optimal solution prematurely. Experimental results show that the proposed method can meet the delay requirements and reduce the total energy consumption of the network.