For the cutting stock problem of circular parts which is widely existed in many manufacturing industries， a new parallel genetic algorithm for cutting stock was proposed to maximize the material utilization within a reasonable computing time， namely Parallel Genetic Blanking Algorithm （PGBA）. In PGBA， the material utilization rate of cutting plan was used as the optimization objective function， and the multithread was used to perform the genetic manipulation on multiple subpopulations in parallel. Firstly， a specific individual coding method was designed based on the parallel genetic algorithm， and a heuristic method was used to generate the individuals of population to improve the search ability and efficiency of the algorithm and avoid the premature phenomena. Then， an approximate optimal cutting plan was searched out by adaptive genetic operations with better performance. Finally， the effectiveness of the algorithm was verified by various experiments. The results show that compared with the heuristic algorithm proposed in literature， PGBA takes longer computing time， but has the material utilization rate greatly improved， which can effectively improve the economic benefits of enterprises.