This work presents an innovative approach to calculate the eigenpairs of linear differential operators (LDO), employing genetic programming (GP) and symbolic computation. In the current literature, the Finite Element Method (FEM) and the Finite Difference Method (FDM) are more commonly used. Such methods use discretization to convert the LDO to a finite matrix, therefore causing loss of accuracy and presence of spurious solutions. Additionally, if the domain of the LDO was changed, the eigenpairs would need to be recalculated, since the matrix representation of the LDO depends on the parameters of the problem. In this context, this work proposes to evolve eigenfunctions analytically using GP, without domain discretization. Hence, it is possible to incorporate the parameter, which makes a obtained solution valid for a class of problems. This text describes the model for normal LDO, applying concepts of multi-tree individuals and symbolic differentiation. The presented model evolves eigenfunctions and, then, calculates the eigenvalues using the Rayleigh quotient. Experiments based on Physics problems show that the proposed technique is able to find the analytical eigenfunctions with the same accuracy of the numerical techniques mentioned above. Finally, the proposed technique is applied to the problem of propagation of electromagnetic waves in oil wells in ULF and UHF. The analytical solutions are given as a function of the diameter and percentage of CaCO in the well. The results show that, for a sufficiently large set of distinct values of the parameters, the technique presents execution time inferior to the FEM, while maintaining its accuracy.