Serious environmental challenges such as global warming and climate change have captured a growing amount of public awareness in the last decade. Besides monetary incentives, the drive for environmental preservation and the pursuit of a sustainable energy source have contributed to an increased recognition of energy usage within the industrial sector. Meanwhile, the challenge of energy efficiency stands out as a major focal point for researchers and manufacturers alike. Efficient assembly line balancing plays a vital role in enhancing production effectiveness. The robotic two-sided assembly line balancing problem (RTALBP) commonly arises in manufacturing facilities that produce large-sized products in high volumes. In this scenario, multiple robots are placed at each assembly line station to manufacture the product. The utilization of robots is extensive within two-sided assembly lines, primarily driven by elevated labour expenses. However, this adoption has resulted in the challenge of increasing energy consumption. Therefore, in this study, a new hybrid genetic algorithm is introduced, incorporating an adaptive local search mechanism. for the mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times. This algorithm has two main objectives: minimizing cycle time (time-based approach) and overall energy consumption (energy-based approach). Depending on managerial priorities, either the time-based or energy-based model can be chosen for different production timeframes.