Many scientific workflows can be modeled as a Directed Acyclic Graph (henceforth mentioned as DAG) where the nodes represent individual tasks and the directed edges represent data and control flow dependency between two tasks. Due to large computational resource requirements, a single cloud cannot meet the requirements of the workflow. Hence, a multi-cloud system, where multiple cloud providers pool their resources together becomes a good solution. The major objectives considered while scheduling the tasks present in a task graph include execution cost and makespan. In this paper, we present Cost Effective Deadline Constrained Evolutionary Scheduler (henceforth mentioned as CEDCES) which aims to minimize the execution cost under a given deadline constraint. CEDCES contains Particle Swarm Optimization-based (henceforth mentioned as PSO) method in its core, however includes novel initialization, crossover, and mutation schemes. Extensive simulation experiments on real-world workflows show that CEDCES outperforms the state-of-art algorithms, in particular, 60.41% on average in terms of execution cost. In cases where the deadline is violated, CEDCES gives the least overshoot in execution time and outperforming the others by 10.96% on average.