Sewage treatment processes are a set of complete solutions for the treatment of urban domestic sewage and industrial wastewater, and they are widely used in various fields. According to the treatment scale, the characteristics of water quality, the environmental functions of the receiving water, and the actual situation and requirements of the local area, the treatment processes of urban domestic sewage should be optimized and determined after the measurement of technological characteristics and economic costs. Thus, the design of urban domestic sewage treatment processes can be regarded as a special form of multi-parameter optimization. Firstly, the process methods of sewage treatment should be sorted out, and the knowledge base of sewage treatment processes should be designed. Secondly, with the parameters and environmental information of each process as inputs, a scheme composed of the processes from the knowledge base is automatically generated based on the sewage treatment process database and the set intelligent algorithm. Specifically, the scheme includes the sequence of each process module, the size of internal components, the operation cost prediction, and the treatment effect. This study adopts the messy genetic algorithm to recommend the sewage treatment processes. The multiplicative inverse of the total cost of the process sequence is taken as the fitness, and the optimized scheme with the lowest cost is automatically generated when multiple pollutant indexes reach the standard. Experimental results show that the messy genetic algorithm can efficiently and accurately recommend the scheme when the process sequence length changes.