Prosody plays an important role in speech communication between humans. Several computer-assisted language learning (CALL) systems with utterance evaluation have been developed so far; however, accuracy of their prosody evaluation is still poor. In this paper, we develop new methods to evaluate rhythm and intonation of English sentence uttered by Japanese learners. The new points of our work are that (1) new prosodic features are added to traditional features, and (2) word importance factors are introduced in the calculation of intonation score. The word importance score is automatically estimated using the ordinary least squares method, and optimized based on word clusters generated by a decision tree. The rhythm evaluator uses two acoustic features, time duration ratio of each word and normalized log-power. From the experiments, correlation coefficient (±1.0 denotes the best correlation) between the rhythm score given by native speakers and the system was -0.55. On the other hand, a conventional feature (pause insertion error rate) gave only -0.11. The intonation evaluator uses four acoustic features, pitch, normalized log-power, and first-order regression coefficients of those two features. Prom the experiments, correlation coefficient between the intonation score given by native speakers and the system was 0.45.