Although originally popularized for structural health monitoring, wireless smart sensors are an attractive alternative to traditional tethered systems for structural control. Their onboard sensing, processing, and wireless communication offer all the components of a feedback control system. However, wireless smart sensors pose unique challenges for the application of centralized control, which is common in most modern control systems. Decentralized control offers several advantages to wireless structural control, including limiting the wireless communication required and the associated slow sampling rate and time delays in the control system. Previous decentralized structural control algorithms, both Ad-Hoc and Heuristic, enforce a spatial sparsity pattern during the design, which is assumed a priori. Therefore, the optimal feedback structure is not considered in the design. This work explores a decentralized optimal LQR design algorithm where the sparsity of the feedback gain is incorporated into the objective function. The control approach is compared to previous decentralized control techniques on the 20-Story control benchmark structure. Sparsity and control requirements are compared to centralized designs. The optimal sparse feedback design offers the best balance of performance, measurement feedback, and control effort. Additionally, the feedback structure identified is not easily identifiable a priori; thus, highlighting the significance of particular measurements in this feedback framework.