The codling moth, Cydia pomonella, is a major pest of apple and pear orchards worldwide. Despite the development of alternative control methods and biopesticides, the use of chemical insecticides remains the most used control method in conventional agriculture. However, an increasing number of resistant phenotypes related to this use are emerging. In this context, the main objective of this study is the identification of genetic markers associated with resistance to different (bio)pesticides in C. pomonella to allow fast and high-throughput molecular detection of resistances in natural populations. An artificial evolution experiment was conducted on a multi-resistant population in order to segregate the alleles associated with resistance to 3 pesticides: spinosad, deltamethrin and chlorantraniliprole. Three different lines were obtained by exercising different selection pressures with the three active substances of interest. Then, RNA and DNA pool-sequencing of the resulting lines were made, in order to identify the genetic variations associated with the resistant phenotypes. Through RNA sequencing, we identified candidate genes within each selected line whose expression profile was associated with insecticide resistance, among which genes known to be involved in detoxification pathways. Variant calling on transcript and whole genome sequencing data, as well as GWAS between resistant and susceptible lines, also allowed us to link polymorphisms in coding and non-coding regions with resistance, providing candidate markers to track resistance. These results contribute to a better understanding of the complex mechanisms associated with insecticide resistance and provide a basis for the development of new resistance detection tools, based on genomic approaches.