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This paper presents a solution framework for the student project allocation (SPA) problem which is based on evolutionary algorithms (EAs), a biologically inspired stochastic, population-based search paradigm. Project-based assessment is a common component of engineering courses that are conducted in universities around the world. In their final year of study, a list of projects is made available by the academic staff and students are required to select a specific number of options from this list. The department then assigns a suitable project to each student such that preferred projects can be allocated to as many students as possible. While student interest is the primary criteria, several additional factors need to be considered such as project prerequisites, load balancing of staff commitments, and other specific university requirements. The allocation problem can therefore be seen as a complex multiobjective problem with multiple constraints. The EA-based project allocation system was recently developed and implemented in a large university department to automate this process, and to improve the matching of students to their desired projects. The solution which provides the highest level of satisfaction in meeting the varied objectives is then used to allocate projects to students. This new automated system is not only able to achieve a very high level of user satisfaction, but is also able to do so in a very short time, resulting in significant time savings.