This paper presents an efficient trap escape strategy in stochastic local search for Satisfiability. The proposed method aims to enhance local search by pro- viding an alternative local minima escaping strategy. Our variable selection scheme provides a novel local minima escaping mechanism to explore new solution areas. Conflict variables are hypothesized as variables recently selected near local min- ima. Hence, a list of backtracked conflict variables is retrieved from local min- ima. The new strategy selects variables in the backtracked variable list based on the clause-weight scoring function and stagnation weights and variable weights as tiebreak criteria. This method is an alternative to the conventional method of se- lecting variables in a randomized unsatisfied clause. The proposed tiebreak method favors high stagnation weights and low variable weights during trap escape phases. The new strategies are examined on verification benchmark and SAT Competi- tion 2011 and 2012 application and crafted instances. Our experiments show that proposed strategy has comparable performance with state-of-the-art local search solvers for SAT.