The pine processionary moth Thaumetopoea pityocampa is a Mediterranean lepidopteran defoliator that experiences a rapid range expansion towards higher latitudes and altitudes due to the current climate warming. Its phenology – the time of sexual reproduction – is certainly a key trait for the local adaptation of the processionary moth to climatic conditions. Moreover, an exceptional case of allochronic differentiation was discovered ca. 15 years ago in this species. A population with a shifted phenology (the summer population, SP) co-exists near Leiria, Portugal, with a population following the classical cycle (the winter population, WP). The existence of this population is an outstanding opportunity to decipher the genetic bases of phenology. No genomic resources were so far available for T. pityocampa. We developed a high-throughput sequencing approach to build a first reference transcriptome, and to proceed with comparative analyses of the sympatric SP and WP. We pooled RNA extracted from whole individuals of various developmental stages, and performed a transcriptome characterisation for both populations combining Roche 454-FLX and traditional Sanger data. The obtained sequences were clustered into ca. 12,000 transcripts corresponding to 9265 unigenes. The mean transcript coverage was 21.9 reads per bp. Almost 70% of the de novo assembled transcripts displayed significant similarity to previously published proteins and around 50% of the transcripts contained a full-length coding region. Comparative analyses of the population transcriptomes allowed to investigate genes specifically expressed in one of the studied populations only, and to identify the most divergent homologous SP/WP transcripts. The most divergent pairs of transcripts did not correspond to obvious phenology-related candidate genes, and 43% could not be functionally annotated. This study provides the first comprehensive genome-wide resource for the target species T. pityocampa. Many of the assembled genes are orthologs of published Lepidoptera genes, which allows carrying out gene-specific re-sequencing. Data mining has allowed the identification of SNP loci that will be useful for population genomic approaches and genome-wide scans of population differentiation to identify signatures of selection.