A new concept of using piezoelectric transducer circuitry with tunable inductance to enhance the performance of frequency-shift-based damage identification method has been recently proposed. While previous work has shown that the frequency-shift information used for damage identification can be significantly enriched by tuning the inductance in the piezoelectric circuitry, a fundamental issue of this approach, namely, how to tune the inductance to best enhance the damage identification performance, has not been addressed. Therefore, this research aims at advancing the state-of-the-art of such a technology by proposing guidelines to form favorable inductance tuning such that the enriched frequency measurement data can effectively capture the damage effect. Our analysis shows that when the inductance is tuned to accomplish eigenvalue curve veering, the change of system eigenvalues induced by structural damage will vary significantly with respect to the change of inductance. Under such curve veering, one may obtain a series of frequency-shift data with different sensitivity relations to the damage, and thus the damage characteristics can be captured more effectively and completely. When multiple tunable piezoelectric transducer circuitries are integrated with the mechanical structure, multiple eigenvalue curve veering can be simultaneously accomplished between desired pairs of system eigenvalues. An optimization scheme aiming at achieving desired set of eigenvalue curve veering is formulated to find the critical inductance values that can be used to form the favorable inductance tuning for multiple piezoelectric circuitries. In the numerical analyses of damage identification, an iterative second-order perturbation-based algorithm is used to identify damages in beam and plate structures. Numerical results show that the performance of damage identification is significantly affected by the selection of inductance tuning, and only when the favorable inductance tuning is used, the locations and severities of structural damages can be accurately identified.