<p>As the design of construction machinery becomes increasingly complex, traditional virtual validation methods struggle to handle multiconstraint and multicomponent assembly tasks efficiently. To overcome this challenge, this study proposes a hybrid optimization framework, termed the greedy selection fireworks algorithm based on priority graph (GSFWA-PG), which integrates priority-based constraint modeling with an efficient search strategy. Assembly constraints are encoded via a priority graph, where each node represents an assembly task and directed edges specify the required execution order between tasks. During sequence generation, only tasks whose prerequisite requirements have been satisfied are considered for selection, thereby preventing invalid assembly sequences. To enhance search efficiency, a greedy selection strategy is incorporated into the fireworks algorithm. This mechanism constructs the assembly sequence incrementally, with one valid task selected at each step based on precedence constraints until a complete sequence of length equal to the total number of tasks is formed. Consequently, the computational complexity for constructing a single feasible sequence is <i>O</i>(<i>n</i>), where <i>n</i> denotes the number of assembly tasks. A customized fitness function is designed to comprehensively evaluate sequence quality. Experimental validation on a loader cab assembly demonstrates that GSFWA-PG achieves the optimal assembly sequence with 100% stability across 20 trials. Notably, it requires an average computation time of only 9.45 ms, outperforming a priority-graph-based genetic algorithm (21.54 ms) and ant colony optimization (117.91 ms) by a substantial margin. These results confirm that GSFWA-PG is a robust and highly efficient solution for complex assembly planning.</p>