http://www.naisit.org/journal/paper/id/140Free and Open Source Software is freely available on the Internet and making use of it, could benefit many higher learning institutions in developing countries. However, before adoption, it is necessary to evaluate the software to see if it meets the requirements of the institution. The evaluation of software involves considering the quality attributes of the software, which can either be evaluated objectively or subjectively, depending on whether the attributes are measured directly or indirectly. To handle the subjectivity of qualitative evaluation an algorithm with inherent computational intelligence was developed. The algorithm, Fuzzy Analytic Hierarchy Process incorporates a modified version of extent analysis. It can tolerate fuzziness, ambiguity, imprecision, uncertainty and ill-illustrated judgements. In addition to the improved Fuzzy Analytic Hierarchy Process development, the Group Fuzzy Analytic Hierarchy Process was developed. Using a specially derived set of end-user centric metrics, the algorithm provides the means for evaluating software according to quality attributes. Software developers, to predict end-user requirements, and to more accurately measure end-user satisfaction can use these quality attributes. Soft Systems Methodology was the preferred research methodology in this investigation as it is well suited for fuzzy problems. The algorithm was validated by evaluating Moodle, a free and open source e-learning system, adopted by a University in Tanzania. Students and staff from the university were involved in providing the subjective opinions about the software. The data collected from the subjective evaluation was captured and using Soft System Methodology, the data was analysed cyclically, improving the algorithm with each cycle. The advantages of the proposed final algorithm are: it is efficient, simple to use and cost-effective. It guides the end user to form an informed decision based on the evaluation results of software. The evaluation results determine whether the outlook is pessimistic, moderate or optimistic.