Evolutionary algorithms have employed the SAW (Stepwise Adaptation ofWeights) method in order to solve CSPs (Constraint Satisfaction Problems). This method originated in hill-climbing algorithms used to solve instances of 3-SAT by adapting a weight for each clause. Originally, adaptation of weights for solving CSPs was done by assigning a weight for each variable or each constraint. Here we investigate a SAW method which assigns a weight for each conflict. Two simple stochastic CSP solvers are presented. For both we show that constraint based SAW and conflict based SAW perform equally on easy CSP samples, but the conflict based SAW outperforms the constraint based SAW when applied to hard CSPs. Moreover, the best of the two suggested algorithms in its conflict based SAWversion performs better than the best known evolutionary algorithm for CSPs that uses weight adaptation, and even better than the best known evolutionary algorithmfor CSPs in general.