Grapevine breeding needs to address two main issues over the next few years: reducing pesticide use and adaptating to climate change. If the selection of new resistant grapevine cultivars has been accelerated, breeding remains a long and costly process for a perennial species such as grapevine. That is why I tested and compared different methodologies for optimizing the breeding of new grapevine varieties. The first is genomic prediction (GP), which relies on the use of molecular markers to train a model to predict genetic values. The second is phenomic prediction (PP), which relies on the use of spectra measured on plant tissues, which is cheaper and more high-throughput than genotyping. I used GP and PP under different configurations to evaluate their interest in breeding programs. For that, I used three grapevine populations with contrasted relatedness, both genotyped and phenotyped. First, I compared univariate and multivariate GP models in a bi-parental population (N=188), on 14 traits related to drought. Multivariate methods did not perform better than univariate ones, and ranking between methods depended on the genetic architecture and heritability of the trait. Secondly, I tested across-population GP (a more applicable configuration for breeding) for 15 traits, by training the prediction model in a diversity panel (N=277) and using 10 bi-parental families of a half-diallel (N=622) as validation sets. For that, I first predicted the average genetic value of each family (for the first selection step of future crosses to be made) and then the genetic values of individuals within each family (for the second selection step of offspring within crosses once they have been realized). Prediction accuracy for these two steps appeared to be satisfying for the application of GP in breeding programs, compared to within-population prediction accuracy. Finally, I tested for the first time the application of PP in grapevine, by using near-infrared spectra measured on wood a