The reactor reloading pattern optimization is a typical combinatorial optimization problem with a huge search space. It is very hard for traditional optimization algorithm to find the global optimal solution in such huge search space. However, for combinatorial optimization problem, the genetic algorithm (GA) provides a very effective solution by its excellent adaptive ability and optimization ability. This paper is focused on the reloading pattern optimization by using GA in a block-type high temperature gas cooled reactor(HTGR) and corresponding programs were written to realize this goal. To improve the calculation accuracy of core physics, the transport calculation with 26 groups is adopted in the core calculation, which will also be time-consuming. To make up for this shortcoming, the parallel optimization of GA is carried out. Finally, a refueling optimization benchmark in a small HTGR is constructed to test the optimization ability of GA. The results show that GA has a good optimization ability and computational stability for reloading pattern optimization in block-type HTGRs.