The increasing popularity of the World Wide Web has made it an attractive medium for advertisers. As more advertisers place Internet advertisements (hereafter also called "ads"), it has become important for Web site owners to maximize revenue through the optimal selection and placement of these ads. Unlike most previous research, we consider a hybrid pricing model, where the price advertisers pay is a function of 1) the number of exposures of the ad and 2) the number of clicks on the ad. The problem is finding an ad schedule to maximize the Web site revenue under a hybrid pricing model. We formulate two versions of the problem - static and dynamic - and propose a variety of efficient solution techniques that provide near-optimal solutions. In the dynamic version, the schedule of ads is changed based on individual user click behavior. We show by using a theoretical proof under special circumstances and an experimental demonstration under general conditions that a schedule that adapts to the user click behavior consistently outperforms one that does not. We also demonstrate that to benefit from observing the user click behavior, the associated probability parameter need not be estimated accurately. For both of these versions, we examine the sensitivity of the revenue with respect to the model parameters.