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A key factor to attain success in any discipline, especially in a field which requires handling large amounts of information and knowledge, is decision making. Most real-world decision-making problems involve a great variety of factors and aspects that sho uld be considered. Making decisions in such environments can often be a difficult operation to perform. For this reason, we need multi-criteria decision-making (MCDM) methods and techniques, which can assist us for dealing with such complex problems. The a im of this paper is to present a new COmbinative Distance-based ASsessment (CODAS) method to handle MCDM problems. To determine the desirability of an alternative, this method uses the Euclidean distance as the primary and the Taxicab distance as the secon dary measure, and these distances are calculated according to the negative - ideal point. The alternative which has greater distances is more desirable in the CODAS method. Some numerical examples are used to illustrate the process of the proposed method. We also perform a comparative sensitivity analysis to examine the results of CODAS and compare it by some existing MCDM methods. These analyses show that the proposed method is efficient, and the results are stable.