Focusing on the task allocation problem in spatial crowdsourcing, with the consideration of the influence of the spatial crowdsourcing workers' service quality on the allocation results, a task allocation strategy with the quality evaluation of worker's service was proposed. Firstly, in each spatio-temporal environment, the evaluation element of spatial crowdsourcing workers was added to establish a multi-objective model that fully considers the service quality and distance cost of the workers. Secondly, the algorithm convergence speed was increased and the global optimization ability was improved by improving the initialization and coding strategy, position movement strategy and neighborhood search strategy of the discrete glowworm swarm optimization algorithm. Finally, the improved algorithm was used to solve the model. Experimental results on the simulated and real datasets show that, compared with other swarm intelligence algorithms, the proposed algorithm can improve the total score of task allocation by 2% to 25% on datasets with different scales. By considering the service quality of workers, the proposed algorithm can effectively improve the efficiency of task allocation and the final total score.