Worker resources are crucial in aircraft final assembly lines (AFAL), which are characterized by extensive manual assembly tasks. The features of AFAL, including resource constraints, makespan balancing, and flexibility in resource allocation, present greater challenges than conventional scheduling problems. This paper addresses the joint optimization problem of worker allocation under a resource dedication policy and scheduling of multi-mode tasks in the AFAL. Bi-objective with lexicographic order of minimizing the cycle time and total worker investment is considered, and an integer programming model is developed to formulate this problem. We propose a resource reallocation embedded genetic algorithm (RReGA) to solve this optimization challenge effectively. Initially, hybrid dispatch rules (HDRs) are employed to estimate the resource-makespan mapping of each workstation, yielding a high-quality initial resource allocation solution. Leveraging these mappings, a resource reallocation method, composed of a resource transfer strategy and a resource recovery strategy, is embedded in the evolutionary process of the genetic algorithm (GA) searching for scheduling solutions at the workstation. The resource transfer strategy is responsible for dynamic resource transfer across workstations, following a novel transfer principle to optimize the cycle time; while the resource recovery strategy aims to meet makespan constraints with the fewest workers to minimize cost. The efficacy and superior performance of the proposed algorithm are validated through comprehensive comparison and ablation experiments, as well as an unbalanced case study.