Abstract: To solve the problem that the encoding process of erasure codes in a distributed storage system incurs high computational complexity, leading to low data write rates, a matrix generation optimization and coding scheduling scheme specifically for Reed-Solomon (RS) Cauchy codes is proposed. First, the selection of the generator matrix is optimized by proposing a greedy low-density scheme. By analyzing the sparsity of the exclusive-or (XOR) operation matrix corresponding to the elements in the Galois Field, an initial row solution is established greedily, and subsequently, the optimized sparse Cauchy matrix is derived through traversal, thereby reducing the computational overhead of the encoding process. Secondly, the GA-CSHR algorithm is proposed to optimize the encoding process of the binary matrix following Cauchy matrix transformation, which uses a genetic algorithm to address the limitations of CSHR and Uber-CSHR algorithms, by caching the intermediate value of the computation process and Heuristically selecting the target block, the number of XOR calculations in the encoding process is reduced. The experimental results show that, compared to the original RS-Cauchy code, the computational complexity of the encoding process is significantly reduced by the proposed scheme based on greedy algorithms and the genetic algorithm.