Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.