Teaching assistantship (TA) assignment is typically a time-consuming and enduring process due to conflicting constraints and multiple objectives. This project involves assigning TA assignments to a large pool of candidates over a smaller set of offered courses, all the while ensuring that fairness is maintained during the assignment. A program was developed to address and optimize based on the given constraints and objectives.  It begins with a data collection phase where all relevant information on the candidates and courses is acquired and then a software program is used to optimize the TA assignment using the Hungarian algorithm. The model was initially formulated as a multi-objective function consisting of three objectives viz., Course/Instructor Preference, Student Preference and Income deviation among candidates. It was then scalarized into a single weighted-sum objective function. The optimizer was tested on candidates applying for teaching assistantship towards three separate semesters, yielding an average of 60-70% matching with the manual assignments that were previously carried out by the department. The statistical dispersion in optimization cost and matching extent towards the manual assignment was observed under the different set of weights. Higher assignment deviations would extend the Pareto domain, which also results in more favorable optimization costs.