<p>The assembly line balancing problem (ALBP) deals with assigning tasks to workstations under precedence constraints to improve assembly line efficiency. When the number of workstations is fixed and the objective is to minimize the cycle time, the problem is known as the simple assembly line balancing problem type II (SALBP-II). When robot arms are assigned to workstations, it becomes the robotic assembly line balancing problem type II (RALBP-II). This study investigates an integrated RALBP-II problem in which automated guided vehicles (AGVs) are responsible for material delivery. Adopting a multi-objective optimization perspective, we simultaneously minimize the cycle time and the total material delivery tardiness to improve assembly line balance and reduce delivery delays. To address this problem, we propose a novel Adaptive Trend Optimization (ATO) algorithm that employs two types of block search: a within-block local search and an outside-block global search. The block length is dynamically adjusted according to the trend factor that reflects the convergence status of the solutions to balance local and global search. The effectiveness of ATO is evaluated on four benchmark instances and compared with four representative metaheuristic algorithms, Particle Swarm Optimization, Genetic Algorithm, Ant Colony Optimization, and Artificial Bee Colony. The experiment uses the Hypervolume (HV) metric to evaluate the algorithm’s solution quality. Moreover, the results show that ATO achieves superior solution quality compared with the other algorithms on this integrated problem.</p>