In 2015, Deb and Abouhawwash proposed Karush Kuhn Tucker Proximity Measure (KKTPM), a metric that can measure how close a point is from being “an optimum”. The smaller the metric the closer the point. Their metric is applicable to both single objective and multiobjective optimization problems. In a single objective problem the metric shows how close a point is from being a “local optimum”, while in multiobjective problems the metric shows how close a point is from being a “local Pareto point”. Exact calculations of KKTPM for each point requires solving a whole optimization problem, which is extremely time consuming. In order to alleviate this problem, the authors of the original work again proposed several approximations to the true KKTPM, namely Direct KKTPM, Projected KKTPM, Adjusted KKTPM and Approximate KKTPM. Approximate KKTPM is simply the average of the former three and is what we call simply “KKTPM”. Moreover, they were able to show that Approximate KKTPM is reliable and can be used in place of the exact one. KKTPM Calculator provides an easy interface through which practitioners can make use of the new metric without delving into the intricacies of proofs and calculations. KKTPM Calculator can be used to calculate all the four approximations and the set of Lagrange multipliers at a specific point. It also supports XML-based input and symbolic mathematical function evaluation among other features.