Optimization problems concern exploration of the best possible solutions to a given problem. The feasible solutions are termed good or bad based on the respective values of the objective function. For optimization problems involving more than one objective functions, absolute comparison among feasible solutions is not as straight forward as is the case with problems involving single objective functions. It can be shown that comparison among all the feasible solutions cannot be accomplished for problems involving more than one objective functions, due to lack of total order among the solutions. Scalarization is the process of transforming the multi-objective vector into a single scalar objective value. Scalarization is a popular approach for solving multi-objective optimization problems. The most prevalent scalarization technique is weighted sum method, which has been shown to be unsuitable for MOO problems having non-convex Pareto Front. It has been shown that non-convex optimization problems can be transformed into better structured problems through monotonic transformations of the objective functions. This work proposes Pairing Functions as an efficient scalarization method. Pairing functions are monotonic, bijective transformations from R2 → R. This makes pairing functions as strictly monotonic functions, which guarantee unique single-valued aggregated objective for unique combinations of the multiple objectives. The effectiveness of the pairing function based scalarization has been demonstrated on bench-mark MOO problems.