The Analytic Hierarchy Process (AHP) is a structured multiple criteria decision making (MCDM) tool in dealing with complex decision making or decisions involving several decision criteria, alternatives and decision makers (DM) with subjective and objective evaluations to come up with a decision. Since group decision making is a common scenario in business and the presence of collective wisdom can lead to better decisions, AHP has been extended to Group Decision Making (GDM). However, there exist limitations in the current AHP GDM algorithms such as the use of imprecise values in preference elicitation, subjective weights to assign for each DM and inferior decision making preferences maximization. Furthermore, existing methodologies in AHP-GDM addressed the lack of precise values by introducing interval judgment which limit the DMs in expressing their preferences. This paper presents a Non-Linear Programming (NLP) model that maximizes the preferences of the DM and maintaining an acceptable level of inconsistency in a GDM setting. It also provides a way to determine the weights to be assigned to each DM based on subjective and objective criteria.