To deal with the combinatorial optimization problem in the era of big data, a parallel Ant Colony Optimization (ACO) algorithm based on Spark, a framework for the distributed memory computing, was presented. To achieve the parallelization of the phase of solution construction in ant colony optimization, a class of ants was encapsulated to a resilient distributed dataset and the corresponding transformation operators were given. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. Under the same experimental environment, the comparison results between MapReduce based ant colony algorithm and the proposed algorithm show that the proposed algorithm significantly improves the optimization speed at least ten times than the MapReduce one.