Weevils are a type of insect with elongated snouts coming from superfamily of Curculionoidea with approximately 97,000 species. Most of them consider pest and cause environmental damages but some kinds like wheat weevil, maize weevil, and boll weevils are famous to cause huge damage on crops, especially cereal grains. This research proposes a novel swarm-based metaheuristics algorithm called Weevil Damage Optimization Algorithm (WDOA) which mimics weevils’ fly power, snout power, and damage power on crops or agricultural products. The proposed algorithm is tested with 12 benchmark unimodal and multimodal artificial landscapes or optimization test functions. Additionally, the proposed WDOA is employed in five engineering problems to check its robustness for problem solving. Problems are Travelling Salesman Problem (TSP), n-Queens problem, portfolio problem, Optimal Inventory Control (OIC) problem, and Bin Packing Problem (BPP). All tests’ functions are compared with widely used benchmark algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Harmony Search (HS) algorithm, Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA), and Differential Evolution (DE) algorithm. Also, all problems are tested with DE, FA, and HS algorithms. The Proposed algorithm showed robustness and speed on all functions and problems by providing precision alongside with reasonable speed.