Trees uses the whole sample, which may create more variance. Furthermore, Random Forests choose the optimum split, whereas Extra Trees select it randomly. However, after selecting the split points, the two algorithms pick up the best one between all subsets of features. Therefore, Extra Trees adds randomization but still has optimization.