Evolutionary MultiTasking Optimization （EMTO） is one of the new methods in evolutionary computing， which can simultaneously solve multiple related optimization tasks and enhance the optimization of each task through knowledge transfer between tasks. In recent years， more and more research on evolutionary multitasking optimization has been devoted to utilizing its powerful parallel search capability and potential for reducing computational costs to optimize various problems， and EMTO has been used in a variety of real-world scenarios. The researches and applications of EMTO were discussed from four aspects： principle， core design， applications， and challenges. Firstly， the general classification of EMTO was introduced from two levels and four aspects， including single-population multitasking， multi-population multitasking， auxiliary task， and multiform task. Next， the core component design of EMTO was introduced， including task construction and knowledge transfer. Finally， its various application scenarios were introduced and a summary and outlook for future research was provided.