Concerning the problem that clustering results are usually required to be balanced in practical applications, a Balanced Clustering algorithm based on Simulated annealing and Greedy strategy (BCSG) was proposed. The algorithm includes two steps:Simulated Annealing Clustering Initialization (SACI) and Balanced Clustering based on Greedy Strategy (BCGS) to improve clustering effectiveness with less time cost. First of all, K suitable data points of data set were located based on simulated annealing as the initial point of balanced clustering, and the nearest data points to each center point were added into the cluster where it belongs in stages greedily until the cluster size reach the upper limit. A series of experiments carried on six UCI real datasets and two public image datasets show that the balance degree can be increased by more than 50 percentage points compared with Fuzzy C-Means when the number of clusters is large, and the accuracy of clustering result is increased by 8 percentage points compared with Balanced K-Means and BCLS (Balanced Clustering with Least Square regression) which have good balanced clustering performance. Meanwhile, the time complexity of the BCSG is also lower, the running time is decreased by nearly 40 percentage points on large datasets compared with Balanced K-Means. BCSG has better clustering effectiveness with less time cost than other balanced clustering algorithms.