In this study, a new metaheuristic optimization algorithm called K-means Optimizer (KO) is introduced. The highlight of KO is the use of K-means clustering technique to identify center vectors—representing areas with good solution potential in the search space. From these vectors, KO deploys two adaptive migration mechanisms, which help expand the exploration scope while enhancing the exploitation ability in potential solution areas. To verify the effectiveness of the method, the KO algorithm is applied on the CEC2020 benchmark functions set. The results obtained from KO are then compared with three popular optimization algorithms today: SCHO, AOA and GWO. Statistics show that KO achieves more outstanding results in most cases. With outstanding performance and good adaptability to complex problems, KO demonstrates its potential for wide application in engineering and optimal design problems requiring high accuracy and computational efficiency.