pymoo is a Python framework for multi-objective optimization that provides a comprehensive set of tools for solving optimization problems with multiple conflicting objectives. It supports a wide range of optimization algorithms, including genetic algorithms, and offers features such as automatic differentiation for gradient computation, parallelization of function evaluations, and visualization of high-dimensional spaces. The framework is modular and customizable, allowing users to extend and modify algorithms by using custom operators. It includes a variety of test problems for different numbers of objectives and constraints, and provides performance indicators to evaluate the quality of results. The framework also supports multi-criteria decision-making to help users select the best solution from a set of non-dominated solutions. The paper describes the architecture of pymoo, including its core modules for problems, optimization, and analytics, and provides an example of how to use the framework to solve a constrained bi-objective optimization problem. The framework is well-documented and includes a starter's guide for users to get started. It is designed to be extendable and supports various programming languages and tools for efficient optimization. The paper also discusses related optimization frameworks in Python and other languages, and highlights the advantages of pymoo in terms of flexibility, customization, and performance.pymoo is a Python framework for multi-objective optimization that provides a comprehensive set of tools for solving optimization problems with multiple conflicting objectives. It supports a wide range of optimization algorithms, including genetic algorithms, and offers features such as automatic differentiation for gradient computation, parallelization of function evaluations, and visualization of high-dimensional spaces. The framework is modular and customizable, allowing users to extend and modify algorithms by using custom operators. It includes a variety of test problems for different numbers of objectives and constraints, and provides performance indicators to evaluate the quality of results. The framework also supports multi-criteria decision-making to help users select the best solution from a set of non-dominated solutions. The paper describes the architecture of pymoo, including its core modules for problems, optimization, and analytics, and provides an example of how to use the framework to solve a constrained bi-objective optimization problem. The framework is well-documented and includes a starter's guide for users to get started. It is designed to be extendable and supports various programming languages and tools for efficient optimization. The paper also discusses related optimization frameworks in Python and other languages, and highlights the advantages of pymoo in terms of flexibility, customization, and performance.