The increase in molecular tools for the genetic engineering of insect pests and disease vectors, such as Anopheles mosquitoes that transmit malaria, has led to an unprecedented investigation of the genomic landscape of these organisms. The understanding of genome variability in wild mosquito populations is of primary importance for vector control strategies. This is particularly the case for gene drive systems, which look to introduce genetic traits into a population by targeting specific genomic regions. Gene drive targets with functional or structural constraints are highly desirable as they are less likely to tolerate mutations that prevent targeting by the gene drive and consequent failure of the technology. In this study we describe a bioinformatic pipeline that allows the analysis of whole genome data for the identification of highly conserved regions that can point at potential functional or structural constraints. The analysis was conducted across the genomes of 22 insect species separated by more than hundred million years of evolution and includes the observed genomic variation within field caught samples of Anopheles gambiae and Anopheles coluzzii, the two most dominant malaria vectors. This study offers insight into the level of conservation at a genome-wide scale as well as at per base-pair resolution. The results of this analysis are gathered in a data storage system that allows for flexible extraction and bioinformatic manipulation. Furthermore, it represents a valuable resource that could provide insight into population structure and dynamics of the species in the complex and benefit the development and implementation of genetic strategies to tackle malaria.