Check-worthiness detection aims at predicting which sentences should be prioritized for fact-checking. A typical use is to rank sentences in political debates and speeches according to their degree of check-worthiness. We present the first direct optimization of sentence ranking for check-worthiness; in contrast, all previous work has solely used standard classification based loss functions. We present a recurrent neural network model that learns a sentence encoding, from which a check-worthiness score is predicted. The model is trained by jointly optimizing a binary cross entropy loss, as well as a ranking based pairwise hinge loss. We obtain sentence pairs for training through contrastive sampling, where for each sentence we find the top most semantically similar sentences with opposite label. Through a comparison to existing state-of-the-art check-worthiness methods, we find that our approach improves the MAP score by 11%.