For a type of repetitive periodic tasks with fixed demand in spatial crowdsourcing， the existing matching algorithms ignore the familiarity required for periodic tasks， so that an online matching algorithm for supporting periodic tasks in spatial crowdsourcing was proposed. Firstly， the online matching problem was regarded as a multiplayer game， with tasks considered as independent participants in the game， and utility functions of the players were determined based on the need of tasks’ preference for matching workers with high familiarity and workers’ preference for tasks with high rewards and short distances， which were then analyzed using Game Theory （GT）. Then， a Simulated Annealing （SA） strategy was introduced into the updating strategy of GT， resulting in the design of GT algorithm based on SA strategy. Finally， a matching with greater total utility was achieved with reaching a Nash equilibrium. Experimental results on real datasets demonstrate that the proposed GT algorithm achieves the highest total utility compared to existing related algorithm， the matching of the proposed GT algorithm has the highest total utility. It can be seen that the proposed GT algorithm achieves matching results that better meet the need of periodic tasks and workers， which can enhance user satisfaction on online spatial crowdsourcing platforms.