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Several tasks must be performed for a software development project to be completed successfully. Task allocation is a complex task that significantly impacts the project's success. Techniques have been proposed over the years to support this step, aiming to minimize cost and development time and to reduce the negative impact of team members leaving the project. In this context, the Truck Factor (TF) is a metric that can determine the risk to a project and can be used when distributing tasks among team members. The TF concerns the distribution of knowledge about the project among the development team members, ensuring that knowledge is not concentrated in only one part of the team. This theme is relevant nowadays since team rotation has become frequent due to the increasing demand for software in recent years. Member allocation in software teams does not have an exact solution since it is an NP-hard problem. Thus, Search-Based Software Engineering (SBSE) techniques, which can apply optimization algorithms such as genetic algorithms, have been used in several types of research to solve this class of problem over the years. In multi-agent environments, a certain number of agents have perception and communicate to achieve their goals. Researchers in the multi-agent field use simulated environments to validate their research since the modeling and simulation must consider the main variables of a real environment. Therefore, this work proposes to build a multi-agent simulation using SBSE techniques to minimize the impacts caused by the Truck Factor in a software development team. In the simulations, we model different configurations for the software development teams. Through statistical analysis and hypothesis testing, our results show that the proposed approach minimizes the impacts caused by task allocation in software development teams by around 25 % when considering the TF metric during task allocation.