In endemic areas of Chagas disease, the protozoan Trypanosoma cruzi circulates in different ecotopes: in the wild, in the peridomicillary and in the domestic. A Chagasic outbreak was recently reported in 2018 in the State of Rio Grande do Norte RN, where Triatoma brasiliensis is the most important vector of Chagas disease. In the present study, populations of different ecotopes in the Rio Grande do Norte RN and Paraíba PB, where there are records of T. brasiliensis that were analyzed using geometric morphometry (with 13 landmarks on the right wings) and the molecular marker cytochrome b. The morphometric analyses were based on 698 individuals and the molecular analyzes on 220 individuals from populations of eight municipalities, ranging 240 km on the east-west axis and 95 km on the north-south axis. For all pairs of populations, the Mahalanobis distances (DM) and the molecular differentiation coefficient (ΦST) were calculated. The results were illustrated in DM, ΦST dendrograms, in addition to factorial maps of principal components (PCA) and canonical variate (CVA) analysis. The Mantel test was applied to verify the correlations between the matrices obtained. Morphometric analysis showed that sexual dimorphism exists, with females being larger than males, with a significant Mahalanobis distance (P <0.0001). It was also observed that the peridomestic insects were larger than the wild insects (P <0.0001). To test the role of ecotypic versus geographic variation in the distribution of morphometric and genetic variability, different groupings were made with the populations. Both markers showed statistically significant differentiation, except between some groups within the district of Patos, which comprises geographically associated municipalities. Mantel tests correlating geographic and morphometric or genetic distances showed low conformity (R 2 <0.35), indicating that factors, other than isolation by distance acted in the distribution of the variation found. The factorial maps with the CVA defined the populations only in geographic microscale. This pattern is possibly the result of demographic events in populations collected at the same point, such as bottlenecks or a founding effect.