This study introduces a new optimization method called K-means Optimizer (KO). The special feature of this algorithm lies in the combination of K-means clustering to determine the centroid vectors—representing the regions with high potential in the search space for solutions. Based on those centroids, the algorithm applies two flexible moving strategies, allowing it to both explore new regions and effectively exploit known regions, in order to find the best solution. To evaluate the effectiveness, the KO algorithm is applied to the optimization problem of a 26-storey truss tower structure with a total of 942 bars and 244 nodes, using 59 design variables. Then, the results from KO are compared with two other popular optimization algorithms, ETO and PSO. The results show that the KO algorithm achieves the smallest optimal value and converges faster than the other two methods. Specifically, KO ranks first in performance among the three algorithms, demonstrating its ability to solve optimization problems efficiently. This shows that KO not only performs well on complex models, but is also a reliable choice for engineering problems that require high accuracy and computational efficiency.