Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, since high levels of inbreeding could lead into a whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies, but discovering highly variable regions across species’ genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify and design PCR primers for amplification of SSR loci from a multi-individual enriched microsatellite library. The pipeline takes as input a fastq file containing sequences from NGS and returns a text file with information regarding the microsatellite markers, including number of alleles in the population, the melting temperature and the respective product of primer sets to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers is able to reduce significantly a manual analysis that takes about 24 hours to 2 minutes, while keeping the same quality of the results.