The continuous expansion of urban spatial scale has made suburban railways play an increasingly important role in travel within metropolises and agglomerations. With the suburban railway interoperability and networked operation, organization becomes more complex, and the impact range of disturbances becomes larger. Simple solutions like adjusting section running time and compressing station stopping time are insufficient to quickly recover delays. This article aims to quickly derive an unplanned skip-stop scheme for suburban railway trains through the use of the Particle Swarm Optimization (PSO) algorithm. First, this article analyzes the demand for unplanned skip-stop scenarios in suburban railways and defines the model parameters, determining the calculation relationships between the parameters. Then, it constructs an integrated objective function and train operation constraints, considering passenger delay waiting time and delay impact duration. A PSO algorithm is designed to solve the model. The article conducts a case study using the peak passenger flow of the Shanghai Jiamin Line. The results show that the generated skip-stop scheme has high usability and fast iterative convergence, ensuring the quality of passenger travel services and providing a reference for the intelligent upgrade of suburban railway dispatch command systems.