Abstract: The traditionally attributed community search problem only studies whether the query attribute exists in the resulting community. To address the needs of complex query scenarios, attributed community search problem under combined query condition is studied. We gave multiple attribute sets, the minimum number of each attribute and the upper limit of community size, and we searched for a community with the maximal number of the minimum degree of nodes. This paper proposed a general algorithm solution framework and two optimization methods: search space optimization based on attribute features to reduce the search space; search order optimization based on structural features to improve algorithm efficiency further by adjusting the search order. Experimental results show that the algorithm can find the attributed community that meets the combined query condition. After two optimizations, the optimized algorithm’s efficiency is 2~3 times higher than the original algorithm on large datasets, and memory overhead is reduced by about 50%.