As pointed out by Morgan and Rubin (2012), chance imbalances often exist in covariate distributions between treatment groups. Not surprisingly, such covariate imbalances also occur in randomized survey experiments. More importantly, the covariate imbalances happen not only between different treatment groups, but also between the sampled experimental units and the overall population of interest. We propose a two-stage rerandomization design that can actively avoid these undesirable covariate imbalances at both the sampling and treatment assignment stages. We further develop asymptotic theory for rerandomized survey experiments, demonstrating that rerandomization provides better covariate balance, more precise estimates of treatment effects, and shorter confidence intervals that are still asymptotically valid. We also propose covariate adjustment to deal with the remaining covariate imbalance after rerandomization, show that it can further improve both the sampling and estimated efficiency, and connect it to usual regression models with least squares estimates.