Multicriteria optimization and decision making is a field of computer science and operations research that deals with problems involving multiple conflicting objectives. These problems often require finding good compromises rather than ideal solutions where all objectives are optimally met. The field includes techniques for computing Pareto optimal solutions, which are solutions that are not dominated by any other solution. This lecture notes provide an introduction to multicriteria optimization and decision analysis, based on the MSc computer science course "Multicriteria Optimization and Decision Analysis" at Leiden University. The course covers the foundations of multicriteria optimization, including orders and dominance, landscape analysis, optimality conditions, scalarization methods, and algorithms for Pareto optimization. The course also discusses human-centric aspects of decision making and the selection, adaptation, and evaluation of multicriteria optimization tools. The lecture notes are structured to introduce the topic in a didactic manner, starting from simple concepts such as linear programming and single-point methods, and advancing to more complex concepts such as optimality conditions for nonlinear optimization and set-oriented solution algorithms. The material is designed to be accessible to MSc students who do not study mathematics as their core discipline, with an emphasis on mathematical modeling and foundations rather than specific algorithms. The course also includes case studies from various application domains, such as economy, engineering, medicine, and social science. The lecture notes provide a detailed introduction to the foundations of multicriteria optimization and decision analysis, as well as a starting point for further study in this interdisciplinary field.Multicriteria optimization and decision making is a field of computer science and operations research that deals with problems involving multiple conflicting objectives. These problems often require finding good compromises rather than ideal solutions where all objectives are optimally met. The field includes techniques for computing Pareto optimal solutions, which are solutions that are not dominated by any other solution. This lecture notes provide an introduction to multicriteria optimization and decision analysis, based on the MSc computer science course "Multicriteria Optimization and Decision Analysis" at Leiden University. The course covers the foundations of multicriteria optimization, including orders and dominance, landscape analysis, optimality conditions, scalarization methods, and algorithms for Pareto optimization. The course also discusses human-centric aspects of decision making and the selection, adaptation, and evaluation of multicriteria optimization tools. The lecture notes are structured to introduce the topic in a didactic manner, starting from simple concepts such as linear programming and single-point methods, and advancing to more complex concepts such as optimality conditions for nonlinear optimization and set-oriented solution algorithms. The material is designed to be accessible to MSc students who do not study mathematics as their core discipline, with an emphasis on mathematical modeling and foundations rather than specific algorithms. The course also includes case studies from various application domains, such as economy, engineering, medicine, and social science. The lecture notes provide a detailed introduction to the foundations of multicriteria optimization and decision analysis, as well as a starting point for further study in this interdisciplinary field.