To address the problem of aircraft mission instruction sequence generation and optimization， we propose a hierarchical-constraint mission instruction sequence processing framework， and further design a sequencing？ method that integrates topological optimization and the priority-encoded genetic algorithm. First， instructions and their constraints are modeled as a directed graph； virtual nodes are introduced to replace Strongly Connected Components （SCCs） in the graph， achieving cycle elimination. Then， for the generated Directed Acyclic Graph （DAG）， a basic initial framework of the instruction sequence is constructed through topological optimization. For the extracted strongly connected components， the priorities of their nodes are encoded and used as the gene indexes of crossover objects， thereby iteratively generating optimized instruction sequence snippets. Finally， the snippets are integrated into the initial framework to achieve the generation and optimization of the mission instruction sequence. Simulation results display that in the scenarios with instruction sets of varying scales and complexities， the proposed method significantly reduces the generation time and compresses the length of instruction sequences compared to other encoding methods.