A classification method for evolutionary data streams is proposed. By utilizing the similarity criterion between the data distribution within the adjacent evolutionary windows and the related knowledge of counterexamples effectively, a enhanced objective function for the optimization problem is proposed. Meanwhile, the solution for the optimization problem is also derived. Both the maximal margin criterion in each evolution window and global optimization of the whole evolutionary data stream are considered, the counterexamples are also fully utilized. The new method learns decision hyperplanes successfully. The experiments on the artificial and real datasets demonstrate the effectiveness of the proposed method.