First of all, if you didn't preprocess your data well, if it doesn't have a lot of clusters, or if there are outliers, the clustering algorithms may fail to work well. So maybe that's happening for you.
Also, measures such as sum-of-squares by definition should decrease with increasing number of clusters. The optimum is often achieved when every object is it's own cluster - a useless result. Thus, do not rely on the measures too much. They can guide you to interesing results (when the measure changed rapidly) and they can detect similar results (little change, not interesting) but in the end, inspect the result manually, don't rely on an evaluation number to tell you what is good - it cannot.