Job Shop Scheduling has been considered as one of the major challenges in industrial manufacturing setups. Conventional and advanced methods have attempted in various research directions to solve such problems. This work is strongly related to the featured multitasking manufacturing cell which is to be designed to produce more than one product. Previous version of this thesis aimed only on the production flow involving one product. But simultaneous operations in the manufacturing cell poses a lot of challenges in terms of design, scheduling, and energy consumption. This study conducts an experimentation utilizing current framework of Reinforcement Learning developed by previous work, for achieving better results in manufacturing two products by same resources. An objective for this optimization scheduling problem is mainly minimizing the total makespan and energy consumption. The model of an integrated cell is built in Plant Simulation Discrete Event Simulation software for realizing the results obtained from training. Various production disturbances have been studied and at least one is tried to incorporate in the experimentation. This thesis is conducted as a part of joint project between University West and Volvo group trunks operations.