The Evaluation based on Distance from Average Solution (EDAS) method is a prominent Multi-Criteria Decision-Making (MCDM) technique used to rank alternatives based on their distance from an average solution. While EDAS is effective and requires fewer calculations than other MCDM methods, it suffers from the rank reversal paradox. This paradox occurs when minor changes in the input data lead to significant and illogical shifts in the rankings of alternatives, undermining the reliability of the decision-making process. We propose the Rank Reversal Free EDAS (RRF-EDAS) approach to address this issue. RRF-EDAS modifies the traditional EDAS method by introducing static decision boundaries for criteria determined by experts. These boundaries define each criterion’s minimum and maximum values, creating a stable reference point for normalizing the decision matrix. This adjustment prevents the rank reversal paradox by ensuring that the normalization process is based on fixed values rather than dynamic changes in the data. The proposed RRF-EDAS approach was tested using Spearman’s weighted correlation coefficient to analyze rank stability. Results show that RRF-EDAS effectively eliminates rank reversal, providing more consistent and reliable rankings than the classical EDAS method. While using fixed boundaries has limitations in dynamic environments, RRF-EDAS offers a robust solution for scenarios where stability and trust in the ranking process are crucial.