Directed evolution aims to accelerate natural evolution process in vitro or in vivo through iterative cycles of genetic diversification and screening or selection. It has been one of the most solid and widely used tools in protein engineering. This review outlines the methods developed 10 years increase the throughput of directed evolution, in vivo gene diversification methods, high-throughput selection and screening methods, continuous evolution strategies, automation-assisted evolution strategies, and AI-assisted protein engineering. To illustrate the significant applications of directed evolution in protein engineering, this review subsequently discusses some remarkable cases to show how directed evolution was used to improve various properties, such as the tolerance to elevated temperature or organic solvent, the activities on non-native substrates, and chemo-, regio-, stereo-, and enantio. In addition, directed evolution has also been widely used to expand the biocatalytic repertories by engineering enzymes with abiotic activities.The enzymes, directed evolution also used to engineer de novo designed enzymes and artificial metalloenzymes with activities comparable or the ones of enzymes. Finally, this review has pointed out that further improving the efficiency and of directed evolution remains. Some advanced evolution and high throughput screening strategies have been succcesfully demostrated in improving the throughput of directed evlutions extensively, but they have been limited to engineering certain protein targets. To resolve those issues, continuously improved computational modeling tools and machine learning strategies can assist us to create a smaller but more accurate library to enhance the probabilies of discovering variants with improved properties. Additionally, laboratorial automation platforms coupled with advanced screening and selection techniques also have great potential to extensively explore the protein landscape by evolving multiple targets continuously in a high throughput manner.