The work aims to reduce the carbon emission of operation in elevated cryogenic storage and establish a cargo optimization model with the largest efficiency, the highest shelf stability and the smallest carbon emissions. Starting from the sequence of goods storage, the overall coding design genetic simulation annealing algorithm was used to solve it. Three schemes of different sizes were selected according to the actual needs and area of cases and the results of the algorithm were analyzed through the Matlab simulation comparison. BIM modeling was conducted to visualize the optimization results of the three schemes. Compared with the simulation anneal algorithm and genetic algorithm, the overall coding design genetic simulation annealing algorithm was 41 and 148 generations earlier. It was converged to optimal solutions, and its computing efficiency was 21.01% and 13.24% higher, indicating that the GASA was suitable for solving the problem of elevated cryogenic storage with concentrated placement of goods in the same species. The recommended size for elevated cryogenic storage was 2.25:1. The optimized location and size can effectively reduce the carbon emission of operation in the storage, ensure the safety and improve the efficiency of storage.