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. Howev- er, 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 tem- perature gas cooled reactor（HTGR） and corresponding programs were written to realize this goal. To improve the calculation ac- curacy of core physics, the transport calculation with 26 groups is adopted in the core calculation, which will also be time-consu- ming. To make up for this shortcoming, the parallel optimization of GA is carried out. Finally, a refueling optimization bench- mark in a small HTGR is constructed to test the optimization ability of GA. The results show that GA has a good optimization a- bility and computational stability for reloading pattern optimization in block-type HTGRs.