In recent years, the rapid depletion of natural resources and irreversible environmental damage have underscored the significance of a Circular Economy. The Circular Economy aims to minimise waste, optimise resource utilisation, and extend product lifespans. However, while the direct recycling of resources produces some positive outcomes, the greenhouse gases emitted during this process remain a substantial threat to the environment. Consequently, remanufacturing has emerged as a favoured option. Disassembly sequence planning (DSP) is a critical initial step in remanufacturing. DSP is classified as an NP-Hard problem, indicating that its solution is computationally complex, rendering traditional methods impractical, particularly for large-scale problems. To address these challenges, metaheuristic optimisation algorithms inspired by natural processes offer effective and efficient solutions. This paper proposes a novel method that integrates the Bees Algorithm (BA) with reinforcement learning (RL) techniques to solve the DSP problem, with a focus on minimising disassembly time. The Bees Algorithm, introduced in 2005 to emulate the foraging behaviour of honeybees, has seen numerous variations. The objective of incorporating RL is to enhance the adaptability of the Bees Algorithm to the search space. To validate the efficiency of the proposed method, the results were compared with the existing BA-based solutions in the literature. This comparative analysis aims to demonstrate the superiority and practical applicability of the new approach in addressing the complexities of DSP.