This chapter introduces advanced methodologies for the modeling and optimization of bimorph devices utilizing cutting-edge piezoactive materials, including functionally graded, piezomagnetoelectric, and thermoelectroelastic systems. Theoretical frameworks tailored to these materials are developed to model their complex behaviors under diverse operational conditions, such as alternating magnetic fields and thermal gradients. These frameworks incorporate spatial variations in material properties, enabling accurate predictions of device performance. The integration of evolutionary computational methods, particularly genetic algorithms, is highlighted as a good approach to solving inverse coefficient problems. These algorithms efficiently calibrate material parameters based on experimental data, thereby improving the precision of the models and facilitating the design of optimized devices. The methodologies presented in this chapter provide insights into the interaction of multifield effects in piezoactive materials, contributing to the advancement of next-generation bimorph systems. By leveraging the interplay of theoretical modeling and computational optimization, this study offers a robust foundation for predicting and enhancing device performance under realistic operational constraints. These contributions underline the potential of integrating advanced material modeling with optimization algorithms for the development of sophisticated piezoelectric devices tailored to specific applications.