The early 2000s saw rapid growthInformation in multiobjective designDesign optimizationOptimization, fueled by advances in evolutionaryEvolutionary algorithmsAlgorithms, Bayesian optimizationOptimization, and surrogateSurrogate modelsModel such as neural networks, Kriging, and Gaussian processes. These methods enabled efficient exploration of complexDesign-space designDesign spacesDesign-space while reducing the cost of expensive functionFunction evaluationsEvaluation. ModelModel-Assisted Multiobjective OptimizationOptimization transformed the fieldField by integrating surrogateSurrogate-assisted techniques to enhance both efficiency and solutionSolution quality. This chapter reviews its historical development, emphasizing key contributions from European researchResearch efforts—including the European Community on ComputationalComputational Methods in Applied Sciences and the INGENET network. We highlight foundational methodologies such as Pareto Efficient Global OptimizationOptimization, Expected Hypervolume Improvement, and confidence-bound and interval-based pre-selection in Bayesian optimizationOptimization. The chapter also examines the impact of these approaches on engineering applications, particularly in computationalComputational fluid dynamicsDynamics andFinite elements finite elementElement method simulationsSimulation, offering a comparative analysisAnalysis of core concepts and their roles in shaping the landscape of modern multiobjective optimizationOptimization.