The paper addresses complexity reduced rate-distortion optimized audio coding under rate constraint. A technique where distortion minimizing coding templates, chosen from a set of templates, are jointly selected for a set of segments. This optimization requires knowledge of rate-distortion pairs for all segments, and for each coding template, which is often costly to obtain. The proposed framework exchanges true rate-distortion pairs with predicted ones, thereby allowing for complexity reduction. The prediction is based on a property vector extracted for each segment, from which distortion predictions, using Gaussian mixture models, are performed. Here, we evaluate the proposed framework in a sinusoidal coding context. The results show that the proposed framework can increase the distortion performance, compared to a fixed sinusoidal coding scheme.