Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and principled assessment design. The generalized nature of the algorithm permits a wide array of test requirements allowing experts to define what to measure and how to measure it and the algorithm is simply a means to an end to support better construct representation. This work also emphasizes the computational algorithm and its ability to scale to support faster computing and better cost-containment in real-world applications than other CAT algorithms. We make a significant effort to consolidate all information needed to build and scale the algorithm so that expert psychometricians and software developers can use this document as a self-contained resource and specification document to build and deploy an operational CAT platform.