This thesis aimed to explore the application of optimization algorithms, through the shape optimization of a rubber bumper, a crucial component in automotive engineering. The ever growing complexity of engineering tasks and the demand for efficient solutions motivated me to investigate how artificial intelligence (AI) and machine learning (ML), integrated into various optimization software, can enhance and speed up the design process. Through the analysis of scientific literature dedicated to rubber materials and optiSlang optimization software, various rubber products were introduced, including their properties, usage cases, advantages, disadvantages, structural, production, and simulation-related limitations, additionally, available techniques and algorithms of optiSlang were explained, their applicability in different industries. The optimization process involves specifying the objective function, in our case, a single goal task, depreciation of a relative error function of a reaction force by modifying the inner and outer diameter of a rubber bumper and comparing it with the known optimum. During the next step specimen’s geometry was created and a finite element model was introduced, including the incorporation of constraints, loads, boundary conditions, and meshing. After confirming the mesh integrity and force convergence through different metrics and graphs, sensitivity analysis was performed. The goal was to find the connection between design parameters and the objective function while narrowing down the search space, which helped to evaluate optimal designs quicker during the comparison of optimization algorithms. Throughout testing, discrete inputs were preferred due to cost and manufacturing limitations, yet continuous variable optimization methods were also employed for their precision. While using optimization algorithms for both types of inputs, results indicated that all optimization methods yield reliable outcomes within reasonable computational timeframes, with the power of standard hardware configurations. The findings indicated the importance of selecting appropriate optimization algorithms based on the specific objectives, computational resources availability, and time constraints of the engineering task. The novel One-Click method was also highlighted, showing rapid results generation capabilities and simultaneous comparison of all available optimization algorithms.