With the rapid development of industrial intelligence technology, the workshop manufacturing mode is gradually transitioning towards flexibility. In the flexible manufacturing mode, the machining process of job breaks through the constraints of fixed equipment and rigid process routes, exhibiting multidimensional flexible characteristics. However, existing researches mainly focus on the flexibility of equipment resources, and there are significant shortcomings in modeling and optimizing the flexibility of processing sequences. To address this problem, this paper first proposes a novel neighborhood structure that takes into account sequential flexible features, which can effectively adjust the processing order of operation in job. On this basis, this paper further proposes a variable neighborhood tabu search algorithm, which can efficiently solve job scheduling problems with sequencing flexibility and obtain high-quality scheduling solutions through the collaborative optimization of variable neighborhood search and tabu strategy. The experimental results show that the solving ability of the proposed algorithm exhibits significant advantages compared to general mathematical programming solvers, providing scientific methodological support for related job shop scheduling problems.