MTO-Platform (MToP) is a comprehensive MATLAB benchmarking platform for evolutionary multitasking (EMT). It provides a unified, extensible framework for defining optimization problems, implementing algorithms, and evaluating performance metrics. The platform includes over 50 multitask algorithms (e.g., MFEA, AT-MFEA, TRADE, SSLT), 200+ multitask problem cases (including CEC, WCCI, and real-world benchmarks), and 20+ performance metrics (IGD, HV, Spread, etc.). It supports single-task, multi-task, many-task, multi-objective, constrained, and competitive optimization scenarios. The system features both a graphical user interface (GUI) and a command-line interface, with parallel execution support and integration with external tools like IOHanalyzer and Python Gym environments for policy search. The architecture is modular: base classes (Problem, Algorithm, Metric) define consistent interfaces, and all components inherit from them. Utility modules (Individual, Initialization, Selection, Operator, Multi-objective sorting) are shared across algorithms, promoting code reuse. The codebase also includes real-world applications such as global trajectory optimization, optimal power flow, sensor coverage, and photovoltaic model parameter extraction. While primarily a research platform, it also serves as a library of reusable evolutionary computation components. The repository is actively maintained (v1.10, references to 2026) but exhibits some technical drift: use of deprecated MATLAB functions (textread), large vendored third-party code (SOM Toolbox), and binary .mat files that may cause compatibility issues. Code duplication exists in problem definitions and some algorithm utilities. Despite these issues, the platform is well-structured, extensively documented, and widely cited in the EMT literature.