随着网络技术的进步，车载网、工业物联网以及5G超高可靠低时延通信（uRLLC）等应用都需要时间敏感网络（TSN）来保证超低延时的确定性数据传输。TSN流量调度需要快速且精确的调度算法，现有的精确式求解方法复杂度高，在大规模联合调度时无法满足实时性。文中设计了一种性能更优的路由优化遗传算法（Routing-GA），结合路由和流量调度约束，能通过优化路由来提高调度算法求解效率，为链路负载均衡调度提供服务。该策略增加了调度的求解空间以及求解灵活性，具备元启发式算法的快速求近最优解特点，能够简单有效地处理大规模TSN路由约束联合调度问题。Routing-GA以时间敏感流最小端到端时延作为优化目标，联合考虑路由和TSN约束，并针对TSN传输问题特性提供一种低复杂度、高效率和高拓展性的遗传算法编码方式。此外，为了提高调度算法的性能，提出针对路由长度及链路负载均衡进行优化的交叉变异机制。实验结果表明所实现的Routing-GA能有效减少端到端时延，显著提高求解质量，进化率可以达到24.42%，平均只需要传统遗传算法（GA）迭代运行时间的12%，从而有效提高了算法的求解性能，满足TSN调度的约束要求。

With the progress of network technology, applications such as vehicle networks, industrial Internet of Things and 5G ultra-reliable low-delay communication (uRLLC) all require TSN to ensure ultra-low delay deterministic data transmission. TSN traffic scheduling requires a fast and accurate scheduling algorithm. The existing accurate solution methods are of high complexity and cannot meet the real-time requirements in large-scale joint scheduling. This paper designed a routing optimization genetic algorithm (Routing-GA) with better performance. Combining routing and traffic scheduling constraints, it can improve the efficiency of scheduling algorithm by optimizing routing and provide services for link load balancing scheduling. This strategy increases the space and flexibility of scheduling, and has the characteristics of fast near-optimal solution of meta-heuristic algorithm. It can deal with large-scale TSN routing constraint joint scheduling problem simply and effectively. Routing-GA takes the minimum end-to-end delay of time-sensitive flow as the optimization objective, considers Routing and TSN constraints jointly, and provides a genetic algorithm coding method with low complexity, high efficiency and high scalability according to the characteristics of TSN transmission problems. In addition, in order to improve the performance of the scheduling algorithm, a crossover mutation mechanism was proposed to optimize the route length and link load balancing. The experimental results show that the realized Routing-GA can effectively reduce the end-to-end delay and significantly improve the solution quality. The evolution rate can reach 24.42%, and the average iteration time of traditional genetic algorithm (GA) is only 12%. It can effectively improve the performance of the algorithm and meet the constraint requirements of TSN scheduling.