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Self-evolution problem of an aircraft-engine assembly workshop with uncertain number of assembly times in knowledgeable manufacturing environment is studied in this article. The whole dynamic self-evolution process is decomposed into a series of static ones by using the rolling horizon procedure. A rolling rule, which is employed to select operations into rolling windows, is designed on the basis of the level priority of each operation. An algorithm for the self-evolution algorithm is then proposed. Due to the deterministic production information, a static decision sub-problem is needed to be solved at each decision point. A general mathematical model of sub-problems is built to minimize the production cost. For solving it, a bi-level genetic algorithm is proposed. In the lower level genetic algorithm, an improved decoding method is also designed by considering production features. Finally, simulation results demonstrate the feasibility and validity of the model and algorithms. Then, computational data indicate that the system with self-evolution capability pays lower costs for production.