For grid-connected neighbors within communities, blockchain-enabled peer-to-peer energy trading proves to be a coherent approach to trade energy from locally produced and distributed renewable energy resources. Effective matching among peers enables enhanced energy efficiency during energy transactions, thereby improving the power quality and preferentially increasing user welfare. The proposed algorithm builds upon work to develop a system of scoring an energy transaction. It employs a McAfee-priced double auction mechanism and assigns the scores based on the preference of factors like price, locality, and the type of energy generation, in addition to the quantity of energy being traded. These transactions are pre-evaluated by the said algorithm to determine the optimal transactional pathway. As a result, the transaction that is finally executed is the one holding the highest cumulative score. The proposed algorithm is simulated over a range of scenarios and tends to boost the user welfare percentile by an average of 75%. From an economic perspective, the algorithm may be implemented in small to large settlements while remaining stable. By reducing power loss, this energy trading algorithm empowers consumers to save around 25% on their energy costs and offers prosumers a 50% increase in revenue.