Vehicular Edge Computing (VEC) has emerged as a promising paradigm to address the growing demand for low-latency computation in vehicular applications, driven by the increasing number of connected vehicles and the massive volume of generated data. However, the highly dynamic nature of VEC environments poses significant challenges for efficient task scheduling. To meet these challenges, this work proposes MOMUS, a multiobjective optimization-based scheduler that leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm to balance conflicting objectives, including maximizing the number of tasks completed within their deadlines, minimizing monetary cost, and reducing system latency. Simulation results show that MOMUS outperforms state-of-the-art VEC scheduling approaches, particularly under high-demand scenarios, achieving higher task completion rates while reducing monetary cost and maintaining acceptable latency.